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WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",159636,2,"2026-04-17T23:33:34",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":79,"owner_website":80,"owner_url":81,"languages":78,"stars":82,"forks":83,"last_commit_at":84,"license":85,"difficulty_score":86,"env_os":87,"env_gpu":88,"env_ram":88,"env_deps":89,"category_tags":92,"github_topics":94,"view_count":32,"oss_zip_url":78,"oss_zip_packed_at":78,"status":17,"created_at":107,"updated_at":108,"faqs":109,"releases":120},8748,"Cartus\u002FAutomated-Fact-Checking-Resources","Automated-Fact-Checking-Resources","Links to conference\u002Fjournal publications in automated fact-checking (resources for the TACL22\u002FEMNLP23 paper).","Automated-Fact-Checking-Resources 是一个专注于自动化事实核查（AFC）领域的开源资源库，旨在为研究人员和开发者提供全面、最新的学术文献与工具索引。面对虚假信息泛滥的挑战，该资源库系统性地梳理了从“主张检测”、“证据检索”到“真实性验证”的完整技术链路，帮助用户快速定位高质量的研究成果。\n\n它主要解决了该领域文献分散、分类标准不一以及多模态核查资源难以获取的痛点。通过整合 TACL 2022 和 EMNLP 2023 的两篇权威综述，它将海量论文按任务类型（如自然\u002F人工主张分类、上下文外检测）、数据集、共享任务及模型架构进行了精细化分类。其独特亮点在于紧跟技术前沿，持续更新包括大语言模型（LLM）事实性、LLM 生成文本检测以及多模态事实核查在内的最新研究（涵盖 2024 年顶会论文），并提供了清晰的任务定义框架图。\n\n无论是从事自然语言处理算法研究的学者、需要构建反谣言系统的工程师，还是希望深入了解虚假信息检测机制的学生，都能从中高效获取所需的核心资料。它不仅是一份文献列表，更是一个动态演进的知识图谱，助力社区共同推动事实核查技术的发展。","# Automated Fact-Checking Resources\n\n[![Maintenance](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMaintained%3F-yes-green.svg)](https:\u002F\u002Fgithub.com\u002FCartus\u002FAutomated-Fact-Checking-Literature)\n[![Last Commit](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flast-commit\u002FCartus\u002FAutomated-Fact-Checking-Literature)](https:\u002F\u002Fgithub.com\u002FCartus\u002FAutomated-Fact-Checking-Literature)\n[![Contribution_welcome](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FContributions-welcome-blue)](https:\u002F\u002Fgithub.com\u002FCartus\u002FAutomated-Fact-Checking-Literature\u002Fblob\u002Fmain\u002Fcontribute.md)\n\n\n### Updates: \n- 2024.12: Added a section for Factuality in LLMs. Added papers from EMNLP and NeurIPS 2024.\n- 2024.8: Added papers from WWW, IJCAI, and ACL 2024\n- 2024.6: Added a section for LLM-generated text in Related Tasks. Added papers from EACL, NAACL, AAAI, ICLR 2024\n\n## Overview\nThis repo contains relevant resources from our survey paper [A Survey on Automated Fact-Checking](https:\u002F\u002Fdirect.mit.edu\u002Ftacl\u002Farticle\u002Fdoi\u002F10.1162\u002Ftacl_a_00454\u002F109469\u002FA-Survey-on-Automated-Fact-Checking) in TACL 2022 and the follow up multimodal survey paper [Multimodal Automated Fact-Checking: A Survey](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.13507.pdf) in EMNLP 2023. In these surveys, we present a comprehensive and up-to-date survey of automated fact-checking (AFC) in text and other modalities, unifying various components and definitions developed in previous research into a common framework.  As automated fact-checking research evolves, we will provide timely updates on the survey and this repo.\n\n\n\n- [Task Definition](#task-definition)\n- [Datasets](#datasets)\n  - [Claim Detection and Extraction](#claim-detection-and-extraction-dataset)\n  - [Verdict Prediction](#verdict-prediction-dataset)\n    - [Veracity Classification](#veracity-classification-dataset)\n      - [Natural Claims](#natural-claims)\n      - [Artificial Claims](#artificial-claims)\n    - [Out-of-context Classification](#out-of-context-classification-dataset)\n    - [Manipulation Classification](#manipulation-classification-dataset)\n- [Shared Tasks](#shared-tasks)\n- [Models](#model)\n  - [Claim Detection and Extraction](#claim-detection-and-extraction)\n  - [Verdict Prediction](#verdict-prediction)\n    - [Veracity Classification](#veracity-classification)\n    - [Out-of-context Classification](#out-of-context-classification)\n    - [Manipulation Classification](#manipulation-classification)\n  - [Justification Production](#justification-production)\n- [Relevant Surveys](#relevant-surveys)\n  - [Automated Fact-Checking](#automated-fact-checking)\n  - [Fake News Detection](#fake-news-detection)\n  - [Claim Detection Related](#claim-detection-related)\n  - [Stance Detection](#stance-detection)\n- [Related Tasks](#related-tasks)\n  - [Factuality in LLMs](#factuality-in-llms)\n  - [Detecting LLM-Generated Text](#detecting-llm-generated-text)\n  - [Misinformation and Disinformation](#misinformation-and-disinformation)\n  - [Detecting Previous Claims](#detecting-previous-claims)\n  - [Adversarial Attack](#adversarial-attack)\n- [Tutorials](#tutorials)\n\n\n## Task Definition\nFigure below shows a NLP framework for automated fact-checking (AFC) with text consisting of three stages:  \n1. Claim detection to identify claims that require verification; \n2. Evidence retrievalto find sources supporting or refuting the claim; \n3. Claim verification to assess the veracity of the claim based on the retrieved evidence. \n\n![Framework](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FCartus_Automated-Fact-Checking-Resources_readme_8bddb0e2389e.png)\n\nEvidence retrieval and claim verification are sometimes tackled as a single task referred to asfactual verification, while claim detection is often tackled separately. Claim verificationcan be decomposed into two parts that can be tackled separately or jointly: verdict prediction, where claims are assigned truthfulness labels, and justification production, where explanations for verdicts must be produced.\n\n\nIn the follow up multimodal survey, we extends the first stage with a claim extraction step, and generalises the third stage to cover tasks that fall under multimodal AFC:\n\n![Framework](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FCartus_Automated-Fact-Checking-Resources_readme_5a69f30fc5a0.png)\n\n1. Claim Detection and Extraction: multiple modalities can be required to understand and extract a claim at this stage. Simply detecting misleading content is often not enough – it is necessary to extract the claim before fact-checking it in the subsequent stages.\n2. Evidence Retrieval: similarly to fact-checking with text, multimodal fact-checking relies on evidence to make judgments.\n3. Verdict Prediction and Justification Production: it is decomposed into three tasks considering prevalent ways that multimodal misinformation can be conveyed:\n    - Manipulation Classification: classify misinformative claims with manipulated content or correct claims accompanied by manipulated content.\n    - Out-of-context Classification: detect unchanged content from a different context.\n    - Veracity Classification: classify the veracity of textual claims given retrieved evidence.\n\n## Datasets\n### Claim Detection and Extraction Dataset\n* MR2: A Benchmark for Multimodal Retrieval-Augmented Rumor Detection in Social Media (Hu et al., 2023)\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539618.3591896)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FTHU-BPM\u002FMR2)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSIGIR%202023-blue)\n* FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms (Qi et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.10973.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fictmcg\u002Ffakesv)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse (Hafid et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.07360.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FAI-4-Sci\u002FSciTweets)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCIKM%202022-blue)\n* Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter (Sundriyal et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.04710.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FLCS2-IIITD\u002FDABERTA-EMNLP-2022)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202022-orange)\n* Stanceosaurus: Classifying Stance Towards Multilingual Misinformation (Zheng et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.15954.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FJonathanQZheng\u002FStanceosaurus\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202022-orange)\n* Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media  (Park et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.12382.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202022-orange)\n* CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets (Mohr et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.12164.pdf)\n  [[Dataset]](https:\u002F\u002Fwww.ims.uni-stuttgart.de\u002Fforschung\u002Fressourcen\u002Fkorpora\u002Fbioclaim\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLREC%202021-orange)\n* MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset (Nielsen et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.11684.pdf)\n  [[Dataset]](https:\u002F\u002Fmumin-dataset.github.io\u002Fgettingstarted\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSIGIR%202021-blue)\n* STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media (Rao et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.269.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Ffip-lab\u002FSTANKER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society (Alam et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.findings-emnlp.56.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Ffirojalam\u002FCOVID-19-disinformation)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202021-orange)\n* Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection (Konstantinovskiy et al., 2021)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.08193.pdf)\n* The CLEF-2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News (Nakov et al., 2021)\n  [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-72240-1_75)\n  [[Dataset]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fclef2021-checkthat\u002Ftasks\u002Ftask-1-check-worthiness-estimation)\n* Mining Dual Emotion for Fake News Detection (Zhang et al., 2021)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.01728.pdf)\n  [[Dataset]](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1pjK0BYiiJt0Ya2nRIrOLCVo-o53sYRBV)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202021-blue)\n* Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media (Barrón-Cedeño et al., 2020)\n  [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-58219-7_17)\n  [[Dataset]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fclef2020-checkthat\u002Fdatasets-tools)\n* Citation Needed: A Taxonomy and Algorithmic Assessment of Wikipedia's Verifiability (Redi et al., 2019)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.11116.pdf)\n  [[Dataset]](https:\u002F\u002Ffigshare.com\u002Farticles\u002Fdataset\u002FCitation_Reason_Dataset\u002F7756226)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202019-blue)\n* SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours (Gorrell et al., 2019).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FS19-2147.pdf)\n  [[Dataset]](https:\u002F\u002Fcompetitions.codalab.org\u002Fcompetitions\u002F19938)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSemEval%202019-orange)\n* Joint Rumour Stance and Veracity (Lillie et al., 2019)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FW19-6122.pdf)\n  [[Dataset]](https:\u002F\u002Ffigshare.com\u002Farticles\u002Fdataset\u002FRumourEval_2019_data\u002F8845580)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNoDaLiDa%202019-orange)\n* Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 1: Check-Worthiness (Atanasova et al., 2018)\n  [[Paper]](http:\u002F\u002Fceur-ws.org\u002FVol-2125\u002Finvited_paper_13.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fclef2018-factchecking\u002Fclef2018-factchecking\u002F#subtasks)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202017-orange)\n* Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter (Volkova et al., 2017)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FP17-2102.pdf)\n  [[Dataset]](https:\u002F\u002Faclanthology.org\u002Fattachments\u002FP17-2102.Datasets.zip)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202017-orange)\n* A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates (Gencheva et al., 2017)\n  [[Paper]](https:\u002F\u002Fwww.acl-bg.org\u002Fproceedings\u002F2017\u002FRANLP%202017\u002Fpdf\u002FRANLP037.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fapepa\u002Fclaim-rank)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FRANLP%202017-orange)\n* Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs (Jin et al., 2017)\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3123266.3123454)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACMMM%202017-red)\n* SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours (Derczynski et al., 2017).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FS17-2006.pdf)\n  [[Dataset]](https:\u002F\u002Falt.qcri.org\u002Fsemeval2017\u002Ftask8\u002Findex.php?id=data-and-tools)\n* Detecting Rumors from Microblogs with Recurrent Neural Networks (Ma et al., 2016)\n  [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F16\u002FPapers\u002F537.pdf)\n  [[Dataset]](https:\u002F\u002Fwww.dropbox.com\u002Fs\u002F46r50ctrfa0ur1o\u002Frumdect.zip?dl=0)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202016-purple)\n* Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads (Zubiaga et al., 2016).\n  [[Paper]](https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle?id=10.1371\u002Fjournal.pone.0150989)\n  [[Dataset]](https:\u002F\u002Ffigshare.com\u002Farticles\u002FPHEME_rumour_scheme_dataset_journalism_use_case\u002F2068650)\n* CREDBANK: A Large-Scale Social Media Corpus with Associated Credibility Annotations (Mitra and Gilbert, 2015).\n  [[Paper]](http:\u002F\u002Feegilbert.org\u002Fpapers\u002Ficwsm15.credbank.mitra.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fcompsocial\u002FCREDBANK-data)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICWSM%202015-blue)\n* Detecting Check-worthy Factual Claims in Presidential Debates (Hassan et al., 2015)\n  [[Paper]](https:\u002F\u002Fidir.uta.edu\u002F~naeemul\u002Ffile\u002Ffactchecking-cikm15-hassan-cameraready.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCIKM%202015-blue)\n\n\n### Verdict Prediction Dataset\n#### Veracity Classification Dataset\n##### Natural Claims\n\n* Do Large Language Models Know about Facts? (Xu et al., 2024)\n  [[Paper]](https:\u002F\u002Fopenreview.net\u002Fforum?id=9OevMUdods)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FTHU-BPM\u002FPinocchio)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FTHU-BPM\u002FPinocchio)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICLR%202024-purple)\n* ESCNet: Entity-enhanced and Stance Checking Network for Multi-modal Fact-Checking (Zhang et al., 2024)\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3589334.3645455)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fzfr00\u002FESCNet)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection (Li et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.09092)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FTrustworthyComp\u002Fmcfend)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification (Wührl et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.01360)\n  [[Dataset]](https:\u002F\u002Fwww.ims.uni-stuttgart.de\u002Fforschung\u002Fressourcen\u002Fkorpora\u002Fbioclaim\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202024-orange)\n* AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web (Schlichtkrull et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.13117)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FMichSchli\u002FAVeriTeC)\n  [[Shared Task]](https:\u002F\u002Ffever.ai\u002Ftask.html)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNeurIPS%202023-purple)\n* COVID-VTS: Fact Extraction and Verification on Short Video Platforms (Liu et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.07919.pdf) \n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FFuxiaoLiu\u002FTwitter-Video-dataset) \n  [[Code]](https:\u002F\u002Fgithub.com\u002FFuxiaoLiu\u002FTwitter-Video-dataset) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202023-orange)\n* End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models (Yao et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.12487.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FVT-NLP\u002FMocheg)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSIGIR%202023-blue)\n* Modeling Information Change in Science Communication with Semantically Matched Paraphrases (Wright et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.13001.pdf) \n  [[Dataset]](https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fcopenlu\u002Fspiced) \n  [[Code]](https:\u002F\u002Fgithub.com\u002Fcopenlu\u002Fscientific-information-change) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202022-orange)\n* Generating Literal and Implied Subquestions to Fact-check Complex Claims (Chen et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.06938.pdf) \n  [[Dataset]](https:\u002F\u002Fjifan-chen.github.io\u002FClaimDecomp\u002F) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202022-orange)\n* SciFact-Open: Towards open-domain scientific claim verification (Wadden et al., 2022)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2022.findings-emnlp.347\u002F)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fdwadden\u002Fscifact-open)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202022-orange)\n* CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking (Hu et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.11863.pdf) \n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FTHU-BPM\u002FCHEF) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNNACL%202022-orange)\n* WatClaimCheck: A new Dataset for Claim Entailment and Inference (Khan et al., 2022) \n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2022.acl-long.92.pdf) \n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fnxii\u002FWatClaimCheck) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202022-orange)\n* Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources (Abdelnabi et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.00061.pdf)\n  [[Dataset]](https:\u002F\u002Fs-abdelnabi.github.io\u002FOoC-multi-modal-fc\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCVPR%202022-red)\n* MMM: An Emotion and Novelty-aware Approach for Multilingual Multimodal Misinformation Detection (Gupta et al., 2022)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2022.findings-aacl.43.pdf) \n  [[Dataset]](https:\u002F\u002Fwww.iitp.ac.in\u002F~ai-nlp-ml\u002Fresources.html#MMM_Dataset) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAACL%202022-orange)\n* FactDrill: A Data Repository of Fact-Checked Social Media Content to Study Fake News Incidents in India (Singhal et al., 2022)\n  [[Paper]](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FICWSM\u002Farticle\u002Fview\u002F19384\u002F19156) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICWSM%202022-blue)\n* Evidence-based Fact-Checking of Health-related Claims (Sarrouti et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.findings-emnlp.297.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fsarrouti\u002Fhealthver)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202021-orange)\n* COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic (Saakyan et al., 2021)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.03794.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fasaakyan\u002Fcovidfact)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* Edited Media Understanding Frames: Reasoning About the Intents and Implications of Visual Disinformation (Da et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.158.pdf)\n  [[Code]](https:\u002F\u002Fjeffda.com\u002Fedited-media-understanding)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* Structurizing Misinformation Stories via Rationalizing Fact-Checks (Jiang et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.51.pdf)\n  [[Dataset]](https:\u002F\u002Fshanjiang.me\u002Fresources\u002F#fact-check)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* X-FACT: A New Benchmark Dataset for Multilingual Fact Checking (Gupta and Srikumar, 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.acl-short.86.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Futahnlp\u002Fx-fact)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification (Azevedo et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.findings-acl.4.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Flucas0\u002FLux)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202021-orange)\n* Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection (Li et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.findings-acl.63.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202021-orange)\n* Explainable Automated Fact-Checking for Public Health Claims (Kotonya and Toni, 2020b)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.emnlp-main.623.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fneemakot\u002FHealth-Fact-Checking)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* Fact or Fiction: Verifying Scientific Claims (Wadden et al., 2020).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.emnlp-main.609.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fallenai\u002Fscifact)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* AnswerFact: Fact Checking in Product Question Answering (Zhang et al., 2020)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.emnlp-main.188.pdf)\n  [[Dataset]](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1JAmbLlV0b8Fm03VnNeVEXmROvj1po2lN\u002Fview)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* Explainable Automated Fact-Checking for Public Health Claims (Kotonya and Toni, 2020). \n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.09926)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fneemakot\u002FHealth-Fact-Checking)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* r\u002FFakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection (Nakamura et al., 2020).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.lrec-1.755.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fentitize\u002Ffakeddit)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLREC%202020-orange)\n* CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims (Diggelmann et al., 2020)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.00614.pdf)\n  [[Dataset]](https:\u002F\u002Fwww.sustainablefinance.uzh.ch\u002Fen\u002Fresearch\u002Fclimate-fever.html)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTCCML@NeurIPS%202024-purple)\n* FakeCovid-- A Multilingual Cross-domain Fact Check News Dataset for COVID-19 (Shahi and Nandini, 2020). \n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2006.11343.pdf)\n  [[Dataset]](https:\u002F\u002Fgautamshahi.github.io\u002FFakeCovid\u002F)  \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICWSM%202020-blue)\n* FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media (Shu et al., 2020).\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.01286.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FKaiDMML\u002FFakeNewsNet)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FBigData%202020-blue)\n* A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking (Hanselowski et al., 2019).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FK19-1046.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FUKPLab\u002Fconll2019-snopes-crawling)\n  [[Dataset](https:\u002F\u002Ftudatalib.ulb.tu-darmstadt.de\u002Fhandle\u002Ftudatalib\u002F2081)]\n  [](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCoNLL%202019-orange)\n* MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims (Augenstein et al., 2019).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD19-1475.pdf)\n  [[Dataset]](https:\u002F\u002Fcompetitions.codalab.org\u002Fcompetitions\u002F21163)\n  [](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202019-orange)\n* Fact-Checking Meets Fauxtography: Verifying Claims About Images (Zlatkova et al., 2019)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FD19-1216.pdf)\n  [[Dataset]](https:\u002F\u002Fgitlab.com\u002Fdidizlatkova\u002Ffake-image-detection)\n  [](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202019-orange)\n* FA-KES: A Fake News Dataset around the Syrian War (Salem et al., 2019)\n  [[Paper]](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FICWSM\u002Farticle\u002Fview\u002F3254\u002F3122)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Ffakenewssyria\u002Ffake_news_detection)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICWSM%202019-blue)\n* Fact Checking in Community Forums (Mihaylova et al., 2018)\n  [[Paper]](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI18\u002Fpaper\u002Fview\u002F16780\u002F16082)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fqcri\u002FQLFactChecking)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202018-purple)\n* EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3219819.3219903)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fyaqingwang\u002FEANN-KDD18)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FKDD%202018-blue)\n* Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 2: Factuality  (Barrón-Cedeño et al., 2018)\n  [[Paper]](http:\u002F\u002Fceur-ws.org\u002FVol-2125\u002Finvited_paper_14.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fclef2018-factchecking\u002Fclef2018-factchecking\u002F#subtasks)\n* Integrating Stance Detection and Fact Checking in a Unified Corpus (Baly et al., 2018).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FN18-2004.pdf)\n  [[Dataset]](https:\u002F\u002Falt.qcri.org\u002Fresources\u002Farabic-fact-checking-and-stance-detection-corpus\u002F)\n  [](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202018-orange)\n* A Stylometric Inquiry into Hyperpartisan and Fake News (Potthast et al., 2018)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FP18-1022.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fwebis-de\u002FACL-18)\n  [](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202018-orange)\n* A News Veracity Dataset with Facebook User Commentary and Egos (Santia and Williams, 2018)\n  [[Paper]](https:\u002F\u002Faaai.org\u002Focs\u002Findex.php\u002FICWSM\u002FICWSM18\u002Fpaper\u002Fview\u002F17825\u002F17046)]\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fgsantia\u002FBuzzFace)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICWSM%202018-blue)\n* Sampling the News Producers: A Large News and Feature Data Set for the Study of the Complex Media Landscape (Horne et al., 2018)\n  [[Paper]](https:\u002F\u002Faaai.org\u002Focs\u002Findex.php\u002FICWSM\u002FICWSM18\u002Fpaper\u002Fview\u002F17796\u002F17044)\n  [[Dataset]](https:\u002F\u002Fdataverse.harvard.edu\u002Fdataset.xhtml?persistentId=doi:10.7910\u002FDVN\u002FZCXSKG)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICWSM%202018-blue)\n* Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking (Rashkin et al., 2017).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD17-1317.pdf)\n  [[Dataset]](https:\u002F\u002Fhrashkin.github.io\u002Ffactcheck.html)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202017-orange)\n* “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection (Wang, 2017).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP17-2067.pdf)\n  [[Dataset]](https:\u002F\u002Fsites.cs.ucsb.edu\u002F~william\u002Fsoftware.html)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202017-orange)\n* Credibility Assessment of Textual Claims on the Web (Popat et al., 2016)\n  [[Paper]](http:\u002F\u002Fresources.mpi-inf.mpg.de\u002Fimpact\u002Fweb_credibility_analysis\u002Fcikm2016-popat.pdf)\n  [[Dataset]](https:\u002F\u002Fwww.mpi-inf.mpg.de\u002Fdepartments\u002Fdatabases-and-information-systems\u002Fresearch\u002Fimpact\u002Fweb-credibility-analysis)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCIKM%202016-blue)\n* Emergent: a novel data-set for stance classification (Ferreira and Vlachos, 2016)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FN16-1138.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fwillferreira\u002Fmscproject)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202016-orange)\n* Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News (Rubin et al., 2016)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FW16-0802.pdf) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCADD@ACL%202016-orange)\n* Identification and Verification of Simple Claims about Statistical Properties (Vlachos and Riedel, 2015)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FD15-1312.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fuclnlp\u002FsimpleNumericalFactChecker)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202015-orange)\n* Fact Checking: Task definition and dataset construction (Vlachos and Riedel, 2014)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FW14-2508.pdf)\n  [[Dataset]](https:\u002F\u002Fwww.dropbox.com\u002Fs\u002Fuvwbpjytogqnm68\u002FFactChecking_LTCSS2014_release.ods?dl=0)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLTCSS@ACL%202014-orange)\n* Verification and Implementation of Language-Based Deception Indicators in Civil and Criminal Narratives (Bachenko et al., 2008)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FC08-1006.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCOLING%202008-orange)\n\n\n##### Artificial Claims\n* EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification (Ma et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2310.09754)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fdependentsign\u002FEX-FEVER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* CFEVER: A Chinese Fact Extraction and VERification Dataset (Lin et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.13025)\n  [[Dataset]](https:\u002F\u002Fikmlab.github.io\u002FCFEVER\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* FACTKG: Fact Verification via Reasoning on Knowledge Graphs (Kim et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.06590.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fjiho283\u002FFactKG)\n  [[Dataset]](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1q0_MqBeGAp5_cBJCBf_1alYaYm14OeTk?usp=share_link)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation  (Huang et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.05386.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fkhuangaf\u002FFakingFakeNews)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fkhuangaf\u002FFakingFakeNews)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering (Rani et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.04329.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking (Akhtar et al., 2023)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2023.findings-eacl.30.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fmubasharaak\u002FChartFC_chartBERT)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202023-orange)\n* Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines (Gabriel et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.08790.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fskgabriel\u002Fmrf-modeling)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202022-orange)\n* DialFact: A Benchmark for Fact-Checking in Dialogue (Gupta et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.08222.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fsalesforce\u002FDialFact)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202022-orange)\n* FAVIQ: FAct Verification from Information-seeking Questions (Park et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.05707)\n  [[Dataset]](https:\u002F\u002Ffaviq.github.io\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202022-orange)\n* FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information (Aly et al., 2021)  \n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.05707)\n  [[Dataset]](https:\u002F\u002Ffever.ai\u002Fresources.html)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FRaldir\u002FFEVEROUS)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNeurIPS%202021-purple)\n* Statement Verification and Evidence Finding with Tables (SEM-TAB-FACT) (Wang et al., 2021)\n  [[Dataset]](https:\u002F\u002Fcompetitions.codalab.org\u002Fcompetitions\u002F27748)\n* Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.naacl-main.52.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FTalSchuster\u002FVitaminC)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202021-orange)\n* ParsFEVER: a Dataset for Farsi Fact Extraction and Verification (Zarharan et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.starsem-1.9.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FZarharan\u002FParsFEVER)\n* DanFEVER: claim verification dataset for Danish (Nørregaard and Derczynski, 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.nodalida-main.47.pdf)\n  [[Dataset]](https:\u002F\u002Ffigshare.com\u002Farticles\u002Fdataset\u002FDanFEVER_claim_verification_dataset_for_Danish\u002F14380970)]\n* HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification (Jiang et al., 2020)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.findings-emnlp.309.pdf)\n  [[Dataset]](https:\u002F\u002Fhover-nlp.github.io\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202020-orange)\n* INFOTABS: Inference on Tables as Semi-structured Data (Gupta et al., 2020)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.acl-main.210.pdf)\n  [[Dataset]](https:\u002F\u002Faclanthology.org\u002Fattachments\u002F2020.acl-main.210.Dataset.zip)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202020-orange)\n* TabFact: A Large-scale Dataset for Table-based Fact Verification (Chen et al., 2020)\n  [[Paper]](https:\u002F\u002Fopenreview.net\u002Fpdf?id=rkeJRhNYDH)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fwenhuchen\u002FTable-Fact-Checking)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICLR%202020-purple)\n* Unsupervised Fact Checking by Counter-Weighted Positive and Negative Evidential Paths in A Knowledge Graph (Kim and Choi, 2020)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.coling-main.147.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCOLING%202020-orange)\n* Stance Prediction and Claim Verification: An Arabic Perspective (Khouja, 2020)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.fever-1.2.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Flatynt\u002Fans)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@ACL%202020-orange)\n* Automated Fact-Checking of Claims from Wikipedia (Sathe et al., 2020).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.lrec-1.849.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fwikifactcheck-english\u002Fwikifactcheck-english)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLREC%202020-orange)\n* FEVER: a Large-scale Dataset for Fact Extraction and VERification (Thorne et al., 2018).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FN18-1074.pdf)\n  [[Dataset]](https:\u002F\u002Ffever.ai\u002Fresources.html)]\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202018-orange)\n* Automatic Detection of Fake News (Pérez-Rosas et al., 2018)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FC18-1287.pdf)\n  [[Dataset]](https:\u002F\u002Flit.eecs.umich.edu\u002Fdownloads.html#undefined)]\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCOLING%202018-orange)\n* The Lie Detector: Explorations in the Automatic Recognition of Deceptive Language (Mihalcea and Strapparava, 2009)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FP09-2078.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202009-orange)\n* Finding Streams in Knowledge Graphs to Support Fact Checking (Shiralkar et al., 2017)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1708.07239.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fshiralkarprashant\u002Fknowledgestream)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICDM%202017-blue)\n* Discriminative predicate path mining for fact checking in knowledge graphs (Shi and Weninger, 2016)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1510.05911)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FKBS%202016-blue)\n* Computational fact checking from knowledge networks (Ciampaglia et al., 2015)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1501.03471.pdf)\n\n#### Manipulation Classification Dataset\n* “Image, Tell me your story!” Predicting the original meta-context of visual misinformation(Tonglet et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2408.09939)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FUKPLab\u002F5pils)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* Cross-Domain Audio Deepfake Detection: Dataset and Analysis (Li et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2404.04904)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fleolya\u002FCD-ADD)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* DF-Platter: Multi-Face Heterogeneous Deepfake Dataset (Narayan et al., 2023)\n  [[Paper]](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FNarayan_DF-Platter_Multi-Face_Heterogeneous_Deepfake_Dataset_CVPR_2023_paper.pdf)\n  [[Dataset]](https:\u002F\u002Fiab-rubric.org\u002Fdf-platter-database)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCVPR%202023-red)\n* Detecting and Grounding Multi-Modal Media Manipulation. (Shao et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.02556.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Frshaojimmy\u002FMultiModal-DeepFake)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCVPR%202023-red)\n* FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset (Khalid et al., 2021)\n  [[Paper]](https:\u002F\u002Fdatasets-benchmarks-proceedings.neurips.cc\u002Fpaper_files\u002Fpaper\u002F2021\u002Ffile\u002Fd9d4f495e875a2e075a1a4a6e1b9770f-Paper-round2.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FDASH-Lab\u002FFakeAVCeleb)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNeurIPS%202021-purple)\n* Half-Truth: A Partially Fake Audio Detection Dataset (Yi et al., 2021)\n  [[Paper]](https:\u002F\u002Fwww.isca-archive.org\u002Finterspeech_2021\u002Fyi21_interspeech.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FInterspeech%202019-green)\n* KoDF: A Large-scale Korean DeepFake Detection Dataset (Kwon et al., 2021)\n  [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9710066)\n  [[Dataset]](https:\u002F\u002Fmoneybrain-research.github.io\u002Fkodf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICCV%202021-red)\n* Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics (Li et al., 2020)\n  [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9156368)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fyuezunli\u002Fceleb-deepfakeforensics)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCVPR%202020-red)\n* DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection (Jiang et al., 2020)\n  [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9156686)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FEndlessSora\u002FDeeperForensics-1.0)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCVPR%202020-red)\n* DeepSonar: Towards Effective and Robust Detection of AI-Synthesized Fake Voices  (Wang et al., 2020)\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3394171.3413716)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACMMM%202020-red)\n* FoR: A Dataset for Synthetic Speech Detection (Reimao et al., 2019)\n  [[Paper]](https:\u002F\u002Fbil.eecs.yorku.ca\u002Fwp-content\u002Fuploads\u002F2020\u002F01\u002FFoR-Dataset_RR_VT_final.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSpeD%202019-green)\n* Phonespoof: A New Dataset for Spoofing Attack Detection in Telephone Channel (Lavrentyeva et al., 2019)\n  [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=8682942)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICASSP%202019-green)\n* The Deepfake Detection Challenge (DFDC) Preview Dataset (Dolhansky et al., 2019)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1910.08854.pdf)\n  [[Dataset]](https:\u002F\u002Fdeepfakedetectionchallenge.ai\u002F)\n* The PS-Battles Dataset -- an Image Collection for Image Manipulation Detection (Heller et al., 2018)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1804.04866.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FdbisUnibas\u002FPS-Battles)\n* FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces (Rossler et al., 2018)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1803.09179.pdf)\n  [[Dataset]](https:\u002F\u002Fjustusthies.github.io\u002Fposts\u002Ffaceforensics\u002F)\n\n\n#### Out-of-Context Classification Dataset\n* Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines (Sung et al., 2023)\n [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2310.13859.pdf)\n [[Dataset]](https:\u002F\u002Fgithub.com\u002Fyysung\u002FVMH\u002Ftree\u002Fmaster)\n ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202023-orange)\n* COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning (Aneja et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.06278.pdf)\n  [[Code]](https:\u002F\u002Fshivangi-aneja.github.io\u002Fprojects\u002Fcosmos\u002F)\n  [[Dataset]](https:\u002F\u002Fdocs.google.com\u002Fforms\u002Fd\u002F13kJQ2wlv7sxyXoaM1Ddon6Nq7dUJY_oftl-6xzwTGow\u002Fviewform?edit_requested=true)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* Factify 2: A multimodal fake news and satire news dataset (Suryavardan et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fftp\u002Farxiv\u002Fpapers\u002F2304\u002F2304.03897.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fsurya1701\u002FFactify-2.0)\n* InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection (Fung et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.133\u002F)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fyrf1\u002FInfoSurgeon)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media (Luo et al., 2021)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.05893.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fg-luo\u002Fnews_clippings)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News (Tan et al., 2020)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.07698.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Frxtan2\u002FDIDAN)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* Multimodal analytics for real-world news using measures of cross-modal entity consistency (Müller-Budack et al., 2020)\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3372278.3390670)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FTIBHannover\u002Fcross-modal_entity_consistency)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICMR%202020-red)\n* Deep Multimodal Image-Repurposing Detection (Sabir et al., 2018)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1808.06686.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FEkraam\u002FMEIR)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACMMM%202018-red)\n* Multimedia semantic integrity assessment using joint embedding of images and text (Jaiswal et al., 2017)\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3123266.3123385)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACMMM%202017-red)\n\n## Shared Tasks\n* AVeriTec Shared Task [[7th FEVER Workshop](https:\u002F\u002Ffever.ai\u002Ftask.html)]\n* The Fact Extraction and VERification (FEVER) Shared Task [[5th FEVER Workshop](https:\u002F\u002Ffever.ai\u002F)]\n* Statement Verification and Evidence Finding with Tables (SEM-TAB-FACT) [[Wang et al., 2021](https:\u002F\u002Fcompetitions.codalab.org\u002Fcompetitions\u002F27748)] \n* SciFact Claim Verifiation [[Wadden et al., 2020](https:\u002F\u002Fsdproc.org\u002F2021\u002Fsharedtasks.html#sciver)]\n* Fakeddit Multimodal Fake News Detection Challenge [[Nakamura et al., 2020](https:\u002F\u002Fcompetitions.codalab.org\u002Fcompetitions\u002F25337#learn_the_details)]\n* SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours [[Gorrell et al., 2019](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FS19-2147\u002F)]\n* SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums [[Mihaylova et al., 2019](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FS19-2149\u002F)]\n* A Retrospective Analysis of the Fake News Challenge Stance-Detection Task [[Hanselowski et al., 2018](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FC18-1158\u002F)]\n* The Fact Extraction and VERification (FEVER) Shared Task [[Thorne et al., 2018](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FW18-5501\u002F)]\n* SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours [[Derczynski et al., 2017](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FS17-2006\u002F)]\n* The Fake News Challenge (FNC-1) [[Pomerleau and Rao, 2017](http:\u002F\u002Fwww.fakenewschallenge.org\u002F)]\n\n\n## Models\n\n### Claim Detection and Extraction\n* Deciphering Rumors: A Multi-Task Learning Approach with Intent-aware Hierarchical Contrastive Learning  (Yang et al., 2024)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.256.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* On Fake News Detection with LLM Enhanced Semantics Mining (Ma et al., 2024)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.31.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* Document-level Claim Extraction and Decontextualisation for Fact-Checking (Deng et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.03239)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202024-orange)\n* Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models (Yang et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.02143)\n  [[Code]](https:\u002F\u002Fanonymous.4open.science\u002Fr\u002FJSDRV-F3CE\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* Unveiling Opinion Evolution via Prompting and Diffusion for Short Video Fake News Detection(Zong et al., 2024)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2024.findings-acl.642.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News (Liu et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.09498)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202024-purple)\n* Heterogeneous Subgraph Transformer for Fake News Detection (Zhang et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2404.13192)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection (Tao et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2404.16076)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* T\u003Csup>3\u003C\u002Fsup>RD: Test-Time Training for Rumor Detection on Social Media (Zhang et al., 2024)\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3589334.3645443)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fsocial-rumors\u002FT3RD)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* Dual Graph Networks with Synthetic Oversampling for Imbalanced Rumor Detection on Social Media (Lu et al., 2024)\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3589335.3651494)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* Rumor Mitigation in Social Networks with Deep Reinforcement Learning (Su et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.09217)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* Adapting Fake News Detection to the Era of Large Language Models (Su et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2311.04917)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fmbzuai-nlp\u002FFakenews-dataset)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* An Interactive Framework for Profiling News Media Sources (Mehta et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2309.07384)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fhockeybro12\u002FInteractive_News_Media_Profiling)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* CMA-R:Causal Mediation Analysis for Explaining Rumour Detection (Tian et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.08155)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fltian678\u002Fcma-r)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202024-orange)\n* Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection (Wang et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2401.15509)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202024-orange)\n* Reinforced Adaptive Knowledge Learning for Multimodal Fake News Detection (Zhang et al., 2024)\n  [[Paper]](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29618)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* Unveiling Implicit Deceptive Patterns in Multi-Modal Fake News via Neuro-Symbolic Reasoning (Dong et al., 2024)\n  [[Paper]](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28677)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fhedongxiao-tju\u002FNSLM)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning Approach for Rumor Detection (Cui et al., 2024)\n  [[Paper]](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F27757)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector (Lao et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2312.11023)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fdm4m\u002FFSRU)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking (Yin et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2312.05739)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning (Xu et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.03357)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fxxfwin\u002FNAGASIL)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection (Hu et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2309.12247)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FICTMCG\u002FARG)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* Interpretable Multimodal Misinformation Detection with Logic Reasoning (Liu et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.05964.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fless-and-less-bugs\u002FLogicMD)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors (Qi et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.05241.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FICTMCG\u002FNEED)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection (Hu et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.14728.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FICTMCG\u002FFTT-ACL23)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Causal Intervention and Counterfactual Reasoning for Multi-modal Fake News Detection (Chen et al., 2023)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2023.acl-long.37.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning (Yue et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.12692.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FYueeeeeeee\u002FMetaAdapt)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning (Lin et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.01117.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* Unsupervised Cross-Domain Rumor Detection with Contrastive Learning and Cross-Attention (Ran et al., 2023)\n [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.11945.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* Zoom Out and Observe: News Environment Perception for Fake News Detection (Sheng et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.10885.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FICTMCG\u002FNews-Environment-Perception\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202022-orange)\n* DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media (Sun et al., 2022)\n  [[Paper]](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-6370.SunM.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202022-purple)\n* Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks (Lin et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.786.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media (Rao et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.269.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Ffip-lab\u002FSTANKER)\n   ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection (Sun et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.findings-emnlp.122.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FMengzSun\u002Fdual-inconsistency-rumor-detection-network)\n   ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202021-orange)\n* Active Learning for Rumor Identification on Social Media (Farinneya et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.findings-emnlp.387.pdf)\n   ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202021-orange)\n* Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection (Wei et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.297.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fweilingwei96\u002FEBGCN)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* Adversary-Aware Rumor Detection (Song et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.findings-acl.118.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fyunzhusong\u002FAARD)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202021-orange)\n* Learning Disentangled Latent Topics for Twitter Rumour Veracity Classification (Dougrez-Lewis et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.findings-acl.341.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FJohnNLP\u002FSAVED)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202021-orange)\n* Mining Dual Emotion for Fake News Detection (Zhang et al., 2021).\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3442381.3450004)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FRMSnow\u002FWWW2021)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202021-blue)\n* Claim Check-Worthiness Detection as Positive Unlabelled Learning (Wright and Augenstein, 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.findings-emnlp.43.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fcopenlu\u002Fcheck-worthiness-pu-learning)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202020-orange)\n* Exploiting Microblog Conversation Structures to Detect Rumors (Li et al., 2020).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.coling-main.473.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCOLING%202018-orange)\n* Debunking Rumors on Twitter with Tree Transformer (Ma et al., 2020)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.coling-main.476.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCOLING%202018-orange)\n* VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text (Cheng et al., 2020)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.00816.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fcmxxx\u002FVRoC)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202020-blue)\n* Rumor Detection on Social Media with Graph Structured Adversarial Learning (Yang et al., 2020)\n  [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2020\u002F0197.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202020-purple)\n* Interpretable Rumor Detection in Microblogs by Attending to User Interactions (Khoo et al., 2020)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2001.10667.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fserenaklm\u002Frumor_detection)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202020-purple)\n* Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks (Bian et al., 2020)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2001.06362.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FTianBian95\u002FBiGCN)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202020-purple)\n* Fake News Early Detection: A Theory-driven Model (Zhou et al., 2020).\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3377478)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202020-purple)\n* MVAE: Multimodal Variational Autoencoder for Fake News Detection (Khattar et al., 2019).\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3308558.3313552)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fdhruvkhattar\u002FMVAE)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202019-blue)\n* Fake News Detection on Social Media using Geometric Deep Learning (Monti et al., 2019).\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.06673.pdf)\n* Rumor Detection on Twitter with Tree-structured Recursive Neural Networks (Ma et al., 2018).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FP18-1184.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FmajingCUHK\u002FRumor_RvNN)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202018-orange)\n* Rumor Detection with Hierarchical Social Attention Network (Guo et al., 2018).\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3269206.3271709)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCIKM%202017-blue)\n* A Hybrid Recognition System for Check-worthy Claims Using Heuristics and Supervised Learning (Zuo et al., 2018).\n  [[Paper]](http:\u002F\u002Fceur-ws.org\u002FVol-2125\u002Fpaper_143.pdf)\n* Simple Open Stance Classification for Rumour Analysis (Aker et al., 2017).\n  [[Paper]](https:\u002F\u002Fwww.acl-bg.org\u002Fproceedings\u002F2017\u002FRANLP%202017\u002Fpdf\u002FRANLP005.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FRANLP%202017-orange)\n* NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter (Enayet and El-Beltagy, 2017).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FS17-2082.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSemEval@ACL%202017-orange)\n* Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM (Kochkina et al., 2017).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FS17-2083.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSemEval@ACL%202017-orange)\n* Automatically Identifying Fake News in Popular Twitter Threads (Buntain and Golbeck, 2017).\n  [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8118443)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSmartCloud%202017-blue)\n* Detecting Rumors from Microblogs with Recurrent Neural Networks (Ma et al., 2016).\n  [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F16\u002FPapers\u002F537.pdf)\n  [[Dataset]](https:\u002F\u002Fwww.dropbox.com\u002Fs\u002F46r50ctrfa0ur1o\u002Frumdect.zip?dl=0)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202016-purple)\n\n\n### Verdict Prediction\n#### Veractiy Classification\n* Do We Need Language-Specific Fact-Checking Models? The Case of Chinese (Zhang et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2401.15498)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fcaiqizh\u002FFC_Chinese)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n\n* FinDVer: Explainable Claim Verification over Long and Hybrid-content Financial Documents (Zhao et al., 2024)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.818.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fyilunzhao\u002FFinDVer)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents (Tang et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2404.10774)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FLiyan06\u002FMiniCheck)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* Evidence Retrieval for Fact Verification using Multi-stage Reranking (Malviya et al., 2024)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2024.findings-emnlp.428.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202024-orange)\n* ChartCheck: Explainable Fact-Checking over Real-World Chart Images (Akhtar et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2311.07453)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fmubasharaak\u002FChartCheck)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* Evidence Retrieval is almost All You Need for Fact Verification (Zheng et al., 2024)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2024.findings-acl.551.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments (Yue et al., 2024)\n  [[Paper]]https:\u002F\u002Farxiv.org\u002Fpdf\u002F2406.09815()\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fyueeeeeeee\u002FRAFTS)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202024-orange)\n* MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking(et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2407.13089)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202024-orange)\n* VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning (Niu et al., 2024)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2024.acl-demos.25.pdf)\n  [[Demo]](https:\u002F\u002Fveractscan.newsbreak.com\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDemo@ACL%202024-orange)\n* Event-Radar: Event-driven Multi-View Learning for Multimodal Fake News Detection(et al., 2024)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2024.acl-long.316.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202024-orange)\n* Unified Evidence Enhancement Inference Framework for Fake News Detection (Wu et al., 2024)\n  [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F0723.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202024-purple)\n* Natural Language-centered Inference Network for Multi-modal Fake News Detection (Zhang et al., 2024)\n  [[Paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F0281.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202024-purple)\n* From Creation to Clarification: ChatGPT's Journey Through the Fake News Quagmire (Huang et al., 2024)\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3589335.3651509)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* MSynFD: Multi-hop Syntax aware Fake News Detection (Liang et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.14834)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* Fighting against Fake News on Newly-Emerging Crisis: A Case Study of COVID-19 (Yang et al., 2024)\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3589335.3651506)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FDSAIL-SKKU\u002FFighting_Against_FakeNews_on_Emerging_Crisis-WWW24)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models (Li et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.14623)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* Fact Checking Beyond Training Set (Karisani et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.18671)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fp-karisani\u002FOODFC)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* Language Models Hallucinate, but May Excel at Fact Verification (Guan et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2310.14564)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FJianGuanTHU\u002FLLMforFV)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* Complex Claim Verification with Evidence Retrieved in the Wild (Chen et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.11859)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fjifan-chen\u002FFact-checking-via-Raw-Evidence)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* MAPLE: Micro Analysis of Pairwise Language Evolution for Few-Shot Claim Verification (Zeng et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2401.16282)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FXiaZeng0223\u002FMAPLE)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202024-orange)\n* Rethinking Loss Functions for Fact Verification (Mukobara et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.08174)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fyuta-mukobara\u002FRLF-KGAT)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202024-orange)\n* Comparing Knowledge Sources for Open-Domain Scientific Claim Verification (Vladika et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.02844)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fjvladika\u002FComparing-Knowledge-Sources)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202024-orange)\n* Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment (Zhang et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.02698)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fzcccccz\u002FCausalWalk)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n*  Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables (Gong et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.13028)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FDeno-V\u002FHeterFC)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* Automated Fact-Checking in Dialogue: Are Specialized Models Needed? (Chamoun et al., 2023)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2023.emnlp-main.993.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202023-orange)\n* DECKER: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification (Zou et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.05921.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FAnni-Zou\u002FDecker)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence (Wang et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.18265.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fposuer\u002FCheck-COVID)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction (Fajcik et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.14116.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FKNOT-FIT-BUT\u002FClaimDissector)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Prompt to be Consistent is Better than Self-Consistent? Few-Shot and Zero-Shot Fact Verification with Pre-trained Language Models (Zeng et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.02569.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fznhy1024\u002FProToCo)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Counterfactual Debiasing for Fact Verification (Xu et al., 2023)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2023.acl-long.374.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Fact-Checking Complex Claims with Program-Guided Reasoning (Pan et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.12744.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fmbzuai-nlp\u002FProgramFC)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Bootstrapping Multi-view Representations for Fake News Detection (Ying et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.05741.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* Varifocal Question Generation for Fact-checking (Ousidhoum et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.12400.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202022-orange)\n* ProoFVer: Natural Logic Theorem Proving for Fact Verification (Krishna et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.11357.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTACL%202022-orange)\n* MultiVerS: Improving scientific claim verification with weak supervision and full-document context (Wadden et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.01640.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fdwadden\u002Fmultivers)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@NAACL%202022-orange)\n* Generating Scientific Claims for Zero-Shot Scientific Fact Checking (Wright et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.12990.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fallenai\u002Fscientific-claim-generation)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202022-orange)\n* Automatic Detection of Entity-Manipulated Text Using Factual Knowledge (Jawahar et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.12990.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FUBC-NLP\u002Fmanipulated_entity_detection)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202022-orange)\n* LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification (Chen et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.13577.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fjiangjiechen\u002FLOREN?ref=pythonrepo.com)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202022-purple)\n* Towards Fine-Grained Reasoning for Fake News Detection (Jin et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.15064.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202022-purple)\n* Synthetic Disinformation Attacks on Automated Fact Verification Systems (Du et al., 2021)\n  [[Paper]](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-11986.DuY.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FYibing-Du\u002Fadversarial-factcheck)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202022-purple)\n* Editing Factual Knowledge in Language Models (De Cao et al., 2021)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.08164.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fnicola-decao\u002FKnowledgeEditor)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification (Shi et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.16.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fqshi95\u002FLERGV)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* Students Who Study Together Learn Better: On the Importance of Collective Knowledge Distillation for Domain Transfer in Fact Verification (Mithun et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.558.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification (Zhang et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.290.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FZhiweiZhang97\u002FARSJointModel)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* Table-based Fact Verification with Salience-aware Learning (Wang et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.findings-emnlp.338.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fluka-group\u002FSalience-aware-Learning)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202021-orange)\n* Exploring Decomposition for Table-based Fact Verification (Yang et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.findings-emnlp.90.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Farielsho\u002Fdecomposition-table-reasoning)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202021-orange)\n* Joint Verification and Reranking for Open Fact Checking Over Tables (Schlichtkrull et al., 2021).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.529.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FOpenTableFactChecking)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* Multi-Task Retrieval for Knowledge-Intensive Tasks (Maillard et al., 2021).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.89.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification (Si et al., 2021).\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.01191.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fjasenchn\u002FTARSA)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* A DQN-based Approach to Finding Precise Evidences for Fact Verification (Wan et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.83.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fsysulic\u002FDQN-FV)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* Unified Dual-view Cognitive Model for Interpretable Claim Verification (Wu et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.5.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge (Hu et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.62.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FBUPT-GAMMA\u002FCompareNet_FakeNewsDetection)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* Automatic Fake News Detection: Are Models Learning to Reason? (Hansen et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.acl-short.12.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fcasperhansen\u002Ffake-news-reasoning)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* Exploring Listwise Evidence Reasoning with T5 for Fact Verification (Jiang et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.acl-short.51.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* Multimodal Fusion with Co-Attention Networks for Fake News Detection (Wu et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.findings-acl.226.pdf)  \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202021-orange)\n* A Multi-Level Attention Model for Evidence-Based Fact Checking (Kruengkrai et al., 2021)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.00950.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fnii-yamagishilab\u002Fmla)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202021-orange)\n* Strong and Light Baseline Models for Fact-Checking Joint Inference (Tymoshenko et al., 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.findings-acl.426.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fikernels\u002Freasoning-baselines)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202021-orange)\n* Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020).\n  [[Paper]](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Ffile\u002F6b493230205f780e1bc26945df7481e5-Paper.pdf)\n  [[Code]](https:\u002F\u002Fhuggingface.co\u002Ftransformers\u002Fmodel_doc\u002Frag.html)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNeurIPS%202021-purple)\n* Language Models as Fact Checkers? (Lee et al., 2020).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.fever-1.5.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@ACL%202020-orange)\n* Hierarchical Evidence Set Modeling for Automated Fact Extraction and Verification (Subramanian et al., 2020)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.05111.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FShyamSubramanian\u002FHESM)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* Program Enhanced Fact Verification with Verbalization and Graph Attention Network (Yang et al., 2020).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.emnlp-main.628.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Farielsho\u002FProgram-Enhanced-Table-Fact-Checking)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* Understanding tables with intermediate pre-training (Eisenschlos et al., 2020).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.findings-emnlp.27.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ftapas) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202020-orange)\n* Fine-grained Fact Verification with Kernel Graph Attention Network (Liu et al., 2020).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.acl-main.655.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fthunlp\u002FKernelGAT)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202020-orange)\n* Reasoning Over Semantic-Level Graph for Fact Checking (Zhong et al., 2020).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.acl-main.549.pdf)\n* LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network (Zhong et al., 2020).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.acl-main.539.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202020-orange)\n* Scrutinizer: A Mixed-Initiative Approach to Large-Scale, Data-Driven Claim Verification (Karagiannis et al., 2020) \n  [[Paper]](http:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol13\u002Fp2508-karagiannis.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fgeokaragiannis\u002Fstatchecker) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FVLDB%202020-blue)\n* Unsupervised Question Answering for Fact-Checking (Jobanputra, 2019).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FD19-6609.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fmayankjobanputra\u002FUQA-fever)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202019-orange)\n* GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification (Zhou et al., 2019).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP19-1085.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fthunlp\u002FGEAR)]\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202019-orange)\n* Combining Fact Extraction and Verification with Neural Semantic Matching Networks (Nie et al., 2019).\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.07039.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Feasonnie\u002Fcombine-FEVER-NSMN\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202019-orange)\n* Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task (Stammbach and Neumann, 2019).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD19-6616.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fnecla-ml\u002Ffever2018)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202019-orange)\n* Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks (Ma et al., 2019).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FP19-1244.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202019-orange)\n* BERT for Evidence Retrieval and Claim Verification (Soleimani et al., 2019)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1910.02655.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FASoleimaniB\u002FBERT_FEVER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FECIR%202019-blue)\n* TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim Verification (Yin and Roth, 2018).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FD18-1010.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fyinwenpeng\u002FFEVER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202018-orange)\n* UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification (Hanselowski et al., 2018).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FW18-5516.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FUKPLab\u002Ffever-2018-team-athene)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202018-orange)\n* Team Papelo: Transformer Networks at FEVER (Malon, 2018).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FW18-5517.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fnecla-ml\u002Ffever2018)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202018-orange)\n* QED: A fact verification system for the FEVER shared task (Luken et al., 2018).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FW18-5526.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fjluken\u002FFEVER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202018-orange)\n* UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF) (Yoneda et al., 2018).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FW18-5515.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fuclmr\u002Ffever)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202018-orange)\n* Can Rumour Stance Alone Predict Veracity? (Dungs et al., 2018).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FC18-1284.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCOLING%202018-orange)\n* Varying Shades: Analyzing Language in Fake News and Political Fact-Checking (Rashkin et al., 2017).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FD17-1317.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202017-orange)\n\n\n#### Manipulation Classification\n*\n  [[Paper]]()\n  [[Dataset]]()\n  ****\n\n#### Out-of-Context Classification\n* Multimodal Misinformation Detection by Learning from Synthetic Data with Multimodal LLMs (Zeng et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2409.19656)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202024-orange)\n* SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection (Qi et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.03170)\n  [[Dataset]](https:\u002F\u002Fpengqi.site\u002FSniffer\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCVPR%202024-red)\n* Exploiting Modality-Specific Features for Multi-Modal Manipulation Detection and Grounding (Wang et al., 2024)\n  [[Paper]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10448385&tag=1)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICASSP%202024-green)\n\n  \n### Justification Production\n\n* TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection (Liu et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.07776)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fless-and-less-bugs\u002FTrust_TELLER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom (Wang et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2405.03371)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fwangbo9719\u002FL-Defense_EFND)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* Benchmarking the Generation of Fact Checking Explanations (Russo et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2308.15202)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FLanD-FBK\u002Fbenchmark-gen-explanations)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTACL%202023-orange)\n* “Why is this misleading?”: Detecting News Headline Hallucinations with Explanations (Shen et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.01060.pdf)]\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning (Si et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.01060.pdf)]\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* Explainable Automated Fact-Checking for Public Health Claims (Kotonya and Toni, 2020). \n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.09926)]\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fneemakot\u002FHealth-Fact-Checking)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fneemakot\u002FHealth-Fact-Checking)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* Generating Fact Checking Explanations (Atanasova et al., 2020).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.acl-main.656.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202020-orange)\n* GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (Lu and Li, 2020).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.acl-main.48.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fl852888\u002FGCAN)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202020-orange)\n* DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (Wu et al., 2020).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.acl-main.97.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202020-orange)\n* ExFaKT: A Framework for Explaining Facts over Knowledge Graphs and Text (Gad-Elrab et al., 2019)\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3289600.3290996)\n  [[Code]](https:\u002F\u002Fwww.mpi-inf.mpg.de\u002Fimpact\u002Fexfakt)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWSDM%202019-blue)\n* dEFEND: Explainable Fake News Detection (Shu et al., 2019).\n  [[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3292500.3330935)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FKDD%202019-blue)\n* Explainable Fact Checking with Probabilistic Answer Set Programming\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.09198)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fppapotti\u002Fexpclaim)\n* Where is your Evidence: Improving Fact-checking by Justification Modeling (Alhindi et al., 2018).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FW18-5513.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FTariq60\u002FLIAR-PLUS)]\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202018-orange)\n* DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning (Popat et al., 2018).\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002FD18-1003.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202019-orange)\n\n\n\n\n## Related Tasks\n\n### Factuality in LLMs\n* Long-Form Factuality in Large Language Models (Wei et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.18802)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Flong-form-factuality)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNeurIPS%202024-purple)\n* An Analysis of Multilingual FActScore (Kim et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2406.19276)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* VeriScore: Evaluating the factuality of verifiable claims in long-form text generation (Song et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2406.19276)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FYixiao-Song\u002FVeriScore)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202024-orange)\n* FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (Min et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.14251)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fshmsw25\u002FFActScore)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202023-orange)\n* FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios (Chern et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2307.13528)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FGAIR-NLP\u002Ffactool)\n\n### Detecting LLM Generated Text\n* MiRAGeNews: Multimodal Realistic AI-Generated News Detection (Huang et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2410.09045)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fnosna\u002Fmiragenews)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202024-orange)\n* Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling (Shi et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.09199)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FICTMCG\u002FPOGER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202024-purple)\n* Detecting AI-Generated Sentences in Human-AI Collaborative Hybrid Texts: Challenges, Strategies, and Insights (Zeng et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.03506)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fdouglashiwo\u002FAISentenceDetection)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202024-purple)\n* GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method (Qazi et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.07321)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* Detecting Generated Native Ads in Conversational Search (Schmidt et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.04889)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fwebis-de\u002FWWW-24)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* BUST: Benchmark for the evaluation of detectors of LLM-Generated Text (Cornelius et al., 2024)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2024.naacl-long.444.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002FIDSIA-NLP\u002FBUST)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* GPT-who: An Information Density-based Machine-Generated Text Detector (Venkatraman et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2310.06202)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fsaranya-venkatraman\u002Fgpt-who)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected? (Zhang et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2401.05952)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FDongping-Chen\u002FMixSet)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n\n\n### Misinformation and Disinformation\n* Misinformation with Legal Consequences (MisLC): A New Task Towards Harnessing Societal Harm of Misinformation (Luo et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2410.03829)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fchufeiluo\u002Fmislc)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202024-orange)\n* Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach (Liu et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2311.09630)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202024-orange)\n* Integrating Argumentation and Hate-Speech-based Techniques for Countering Misinformation (Saha et al., 2024)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.622.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fsougata-ub\u002FMisInfoCorrected)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* Simulated Misinformation Susceptibility (SMISTS): Enhancing Misinformation Research with Large Language Model Simulations (Ma et al., 2024)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2024.findings-acl.162\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection (Wan et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.10426)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fwhr000001\u002FDELL)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* The Dynamics of (Not) Unfollowing Misinformation Spreaders (Ashkinaze et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2401.13480)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation (Yue et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.14952)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* Enhancing Society-Undermining Disinformation Detection through Fine-Grained Sentiment Analysis Pre-Finetuning (Pan et al., 2024)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2024.findings-eacl.92.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202024-orange)\n* Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.09683.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fethanm88\u002Fhitl-evaluation-early-misinformation-detection)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation (He et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.06433.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fclaws-lab\u002FMisinfoCorrect)\n  [[Code]](https:\u002F\u002Fgithub.com\u002Fclaws-lab\u002FMisinfoCorrect)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202023-blue)\n* Who Funds Misinformation? A Systematic Analysis of the Ad-related Profit Routines of Fake News sites (Papadogiannakis et al., 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.05079.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202023-blue)\n* Misinformation, Disinformation, and Online Propaganda (Guess and Lyons, 2020)\n  [[Paper]](https:\u002F\u002Fwww.cambridge.org\u002Fcore\u002Fbooks\u002Fsocial-media-and-democracy\u002Fmisinformation-disinformation-and-online-propaganda\u002FD14406A631AA181839ED896916598500\u002Fcore-reader)\n\n\n### Detecting Previous Claims\n* Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document (Shaar et al., 2022)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.07410.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202022-orange)\n* Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims (Sheng et al. 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.425.pdf)\n  [[Code]](https:\u002F\u002Fgithub.com\u002FICTMCG\u002FMTM)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* Claim Matching Beyond English to Scale Global Fact-Checking (Kazemiet al. 2021)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.347.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* The CLEF-2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News (Nakov et al., 2021)\n  [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-72240-1_75)]\n* That is a Known Lie: Detecting Previously Fact-Checked Claims (Shaar et al., 2020)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.acl-main.332.pdf)\n  [[Dataset]](https:\u002F\u002Fgithub.com\u002Fsshaar\u002FThat-is-a-Known-Lie)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202020-orange)\n* COVIDLies: Detecting COVID-19 Misinformation on Social Media (Hossain et al., 2020)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.nlpcovid19-2.11.pdf)\n* Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media (Barrón-Cedeño et al., 2020)\n  [[Paper]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-58219-7_17)\n \n### Adversarial Attack\n* Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions (Hu et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.12077)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* A General Black-box Adversarial Attack on Graph-based Fake News Detectors (et al., 2024) \n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.15744)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202024-purple)\n\n\n## Relevant Surveys\n\n### Automated Fact-Checking\n* Automated Justification Production for Claim Veracity in Fact Checking: A Survey on Architectures and Approaches (Eldifrawi et al., 2024)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.12853)\n* Scientific Fact-Checking: A Survey of Resources and Approaches (Vladika and Matthes, 2023)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.16859.pdf)\n* A Survey on Multimodal Disinformation Detection (Alam et al., 2021) \n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2103.12541.pdf)\n* Automated fact-checking: A survey (Zeng et al., 2021)\n  [[Paper]](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1111\u002Flnc3.12438)\n* Towards Explainable Fact Checking (Isabelle Augenstein, 2021)\n  [[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.10274.pdf)\n* Explainable Automated Fact-Checking: A Survey (Kotonya and Toni, 2020)\n  [[Paper]](https:\u002F\u002Faclanthology.org\u002F2020.coling-main.474.pdf)\n* A Survey on Natural Language Processing for Fake News Detection (Oshikawa et al., 2020).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.lrec-1.747.pdf)\n* A Review on Fact Extraction and VERification: The FEVER case (Bekoulis et al., 2020).\n  [[paper]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.03001)\n* Automated Fact Checking: Task Formulations, Methods and Future Directions (Thorne and Vlachos, 2018).\n  [[Paper]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FC18-1283.pdf)\n\n* A Content Management Perspective on Fact-Checking (Cazalens et al., 2018).\n  [[paper]](https:\u002F\u002Fhal.archives-ouvertes.fr\u002Fhal-01722666\u002Fdocument)\n\n### Fake News Detection\n* A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities (Zhou and Zafarani, 2020).\n[[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3395046)\n* A Survey on Fake News and Rumour Detection Techniques (Bondielli and Marcelloni, 2020).\n[[paper]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0020025519304372?via%3Dihub)\n* Can Machines Learn to Detect Fake News? A Survey Focused on Social Media (da Silva et al. 2019)\n[[Paper]](https:\u002F\u002Fscholarspace.manoa.hawaii.edu\u002Fhandle\u002F10125\u002F59713)\n* Fake News Detection using Stance Classification: A Survey (Lillie and Middelboe, 2019).\n[[paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.00181.pdf)\n* The science of fake news (Lazer et al. 2018) \n[[Paper]](https:\u002F\u002Fscience.sciencemag.org\u002Fcontent\u002F359\u002F6380\u002F1094)\n* Media-Rich Fake News Detection: A Survey (Parikh and Atrey, 2018).\n[[paper]](https:\u002F\u002Fwww.albany.edu\u002F~sp191221\u002Fpublications\u002FFake_Media_Rich_News_Detection_A_Survey.pdf)\n* Fake News Detection on Social Media: A Data Mining Perspective (Shu et al., 2017).\n[[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1708.01967.pdf)\n\n### Claim Detection Related\n* Deep learning for misinformation detection on online social networks: a survey and new perspectives (Islam et al. 2020)\n[[Paper]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007%2Fs13278-020-00696-x)\n* A Survey on Computational Propaganda Detection (Da San Martino et al. 2020). \n[[Paper]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2020\u002F0672.pdf)\n* Social Media, Political Polarization, and Political Disinformation: A Review of the Scientific Literature (Tucker et al., 2018)\n  [[Paper]](https:\u002F\u002Fwww.hewlett.org\u002Fwp-content\u002Fuploads\u002F2018\u002F03\u002FSocial-Media-Political-Polarization-and-Political-Disinformation-Literature-Review.pdf)\n* Detection and Resolution of Rumours in Social Media: A Survey (Zubiaga et al., 2018).\n[[Paper]](http:\u002F\u002Fkddlab.zjgsu.edu.cn:7200\u002Fresearch\u002Frumor\u002FDetection%20and%20Resolution%20of%20Rumours%20in%20Social%20Media_%20A%20Survey.pdf)\n\n### Stance Detection\n* A Survey on Stance Detection for Mis- and Disinformation Identification (Hardalov et al. 2021)\n[[Paper]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2103.00242.pdf)\n* Stance Detection: A Survey (Küçük and Can 2020)\n[[Paper]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3369026)\n\n\n## Tutorials\n* Preventing and Detecting Misinformation Generated by Large Language Models [[Liu et al., SIGIR 2024]](https:\u002F\u002Fsigir24-llm-misinformation.github.io\u002F)\n* Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era [[Nakov and Da San Martino, EMNLP 2020]](https:\u002F\u002Fpropaganda.qcri.org\u002Femnlp20-tutorial\u002F).\n* Detection and Resolution of Rumors and Misinformation with NLP [[Derczynski and Zubiaga, COLING 2020]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.coling-tutorials.4.pdf) [[slides]](https:\u002F\u002Fdocs.google.com\u002Fpresentation\u002Fd\u002F1ZBVPtHcVgJW2c_ibrdVuoCH7sU9ha8NS7Fq9GCnBnls\u002Fedit?usp=sharing).\n* Fact Checking: Theory and Practice [[Dong et al., KDD 2018]](https:\u002F\u002Fshiralkarprashant.github.io\u002Ffact-checking-tutorial-KDD2018\u002F).\n\n\u003C!-- omit in toc -->\n## ⭐ Star History\n\n\u003Ca href=\"https:\u002F\u002Fstar-history.com\u002F#Cartus\u002FAutomated-Fact-Checking-Resources&Date\">\n \u003Cpicture>\n   \u003Csource media=\"(prefers-color-scheme: dark)\" srcset=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FCartus_Automated-Fact-Checking-Resources_readme_b56c89cfedb6.png&theme=dark\" \u002F>\n   \u003Csource media=\"(prefers-color-scheme: light)\" srcset=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FCartus_Automated-Fact-Checking-Resources_readme_b56c89cfedb6.png\" \u002F>\n   \u003Cimg alt=\"Star History Chart\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FCartus_Automated-Fact-Checking-Resources_readme_b56c89cfedb6.png\" \u002F>\n \u003C\u002Fpicture>\n\u003C\u002Fa>\n\n","# 自动化事实核查资源\n\n[![维护中](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FMaintained%3F-yes-green.svg)](https:\u002F\u002Fgithub.com\u002FCartus\u002FAutomated-Fact-Checking-Literature)\n[![最近提交](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flast-commit\u002FCartus\u002FAutomated-Fact-Checking-Literature)](https:\u002F\u002Fgithub.com\u002FCartus\u002FAutomated-Fact-Checking-Literature)\n[![欢迎贡献](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FContributions-welcome-blue)](https:\u002F\u002Fgithub.com\u002FCartus\u002FAutomated-Fact-Checking-Literature\u002Fblob\u002Fmain\u002Fcontribute.md)\n\n\n### 更新：\n- 2024年12月：新增了LLM中的事实性部分。添加了EMNLP和NeurIPS 2024的论文。\n- 2024年8月：添加了WWW、IJCAI和ACL 2024的论文。\n- 2024年6月：新增了相关任务中LLM生成文本的部分。添加了EACL、NAACL、AAAI、ICLR 2024的论文。\n\n## 概述\n本仓库包含了我们发表在TACL 2022上的综述论文《自动化事实核查综述》（A Survey on Automated Fact-Checking）以及后续发表在EMNLP 2023上的多模态综述论文《多模态自动化事实核查综述》（Multimodal Automated Fact-Checking: A Survey）中的相关资源。在这些综述中，我们全面且最新地回顾了文本及其他模态下的自动化事实核查（AFC），并将先前研究中提出的各种组件和定义统一到一个通用框架中。随着自动化事实核查研究的不断发展，我们将及时更新综述内容及本仓库。\n\n- [任务定义](#task-definition)\n- [数据集](#datasets)\n  - [声明检测与提取数据集](#claim-detection-and-extraction-dataset)\n  - [判决预测数据集](#verdict-prediction-dataset)\n    - [真实性分类数据集](#veracity-classification-dataset)\n      - [自然声明](#natural-claims)\n      - [人工声明](#artificial-claims)\n    - [语境外分类数据集](#out-of-context-classification-dataset)\n    - [操纵分类数据集](#manipulation-classification-dataset)\n- [共享任务](#shared-tasks)\n- [模型](#model)\n  - [声明检测与提取](#claim-detection-and-extraction)\n  - [判决预测](#verdict-prediction)\n    - [真实性分类](#veracity-classification)\n    - [语境外分类](#out-of-context-classification)\n    - [操纵分类](#manipulation-classification)\n  - [理由生成](#justification-production)\n- [相关综述](#relevant-surveys)\n  - [自动化事实核查](#automated-fact-checking)\n  - [假新闻检测](#fake-news-detection)\n  - [声明检测相关](#claim-detection-related)\n  - [立场检测](#stance-detection)\n- [相关任务](#related-tasks)\n  - [LLM中的事实性](#factuality-in-llms)\n  - [检测LLM生成文本](#detecting-llm-generated-text)\n  - [虚假信息与错误信息](#misinformation-and-disinformation)\n  - [检测先前声明](#detecting-previous-claims)\n  - [对抗攻击](#adversarial-attack)\n- [教程](#tutorials)\n\n\n## 任务定义\n下图展示了一个基于文本的自动化事实核查（AFC）的NLP框架，包含三个阶段：\n1. 声明检测：识别需要验证的声明；\n2. 证据检索：寻找支持或反驳该声明的来源；\n3. 声明验证：根据检索到的证据评估声明的真实性。\n\n![框架](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FCartus_Automated-Fact-Checking-Resources_readme_8bddb0e2389e.png)\n\n有时，证据检索和声明验证会被合并为一个称为事实验证的任务，而声明检测则通常单独处理。声明验证可以进一步分解为两个部分，既可以分别处理，也可以联合进行：判决预测，即为声明分配真实性标签；以及理由生成，即为判决提供解释。\n\n在后续的多模态综述中，我们将第一阶段扩展为声明提取步骤，并将第三阶段推广至涵盖多模态自动化事实核查的相关任务：\n\n![框架](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FCartus_Automated-Fact-Checking-Resources_readme_5a69f30fc5a0.png)\n\n1. 声明检测与提取：在此阶段，可能需要多种模态来理解和提取声明。仅仅检测误导性内容往往是不够的——必须先提取出声明，才能在后续阶段进行事实核查。\n2. 证据检索：与基于文本的事实核查类似，多模态事实核查同样依赖于证据来进行判断。\n3. 判决预测与理由生成：这一阶段被细分为三个任务，以应对多模态虚假信息常见的传播方式：\n   - 操纵分类：对包含操纵内容的虚假声明，或伴随操纵内容的正确声明进行分类。\n   - 语境外分类：检测来自不同上下文但内容未改变的信息。\n   - 真实性分类：根据检索到的证据，对文本声明的真实性进行分类。\n\n## 数据集\n\n### 声称检测与提取数据集\n* MR2：社交媒体中多模态检索增强型谣言检测基准（Hu 等，2023）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3539618.3591896)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FTHU-BPM\u002FMR2)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSIGIR%202023-blue)\n* FakeSV：短视频平台上虚假新闻检测的丰富社交背景多模态基准（Qi 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2211.10973.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fictmcg\u002Ffakesv)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* SciTweets - 用于检测科学在线话语的数据集与标注框架（Hafid 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.07360.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FAI-4-Sci\u002FSciTweets)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCIKM%202022-blue)\n* 助力事实核查者！Twitter 上声称片段的自动识别（Sundriyal 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.04710.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FLCS2-IIITD\u002FDABERTA-EMNLP-2022)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202022-orange)\n* Stanceosaurus：多语言虚假信息立场分类（Zheng 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.15954.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FJonathanQZheng\u002FStanceosaurus\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202022-orange)\n* 信息操纵检测中的挑战与机遇：对战时俄罗斯媒体的考察（Park 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.12382.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202022-orange)\n* CoVERT：经事实核查的生物医学 COVID-19 推文语料库（Mohr 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2204.12164.pdf)\n  [[数据集]](https:\u002F\u002Fwww.ims.uni-stuttgart.de\u002Fforschung\u002Fressourcen\u002Fkorpora\u002Fbioclaim\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLREC%202021-orange)\n* MuMiN：大规模多语言多模态经事实核查的虚假信息社交网络数据集（Nielsen 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.11684.pdf)\n  [[数据集]](https:\u002F\u002Fmumin-dataset.github.io\u002Fgettingstarted\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSIGIR%202021-blue)\n* STANKER：基于层次粒度注意力掩码 BERT 的堆叠网络，用于社交媒体上的谣言检测（Rao 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.269.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Ffip-lab\u002FSTANKER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* 抗击 COVID-19 信息疫情：建模记者、事实核查员、社交媒体平台、政策制定者和社会各界的观点（Alam 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.findings-emnlp.56.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Ffirojalam\u002FCOVID-19-disinformation)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202021-orange)\n* 向自动化事实核查迈进：开发一致的自动化声称检测标注方案和基准（Konstantinovskiy 等，2021）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.08193.pdf)\n* CLEF-2021 CheckThat! 实验室：检测值得核查的 claims、先前已被事实核查的 claims 和假新闻（Nakov 等，2021）\n  [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-72240-1_75)\n  [[数据集]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fclef2021-checkthat\u002Ftasks\u002Ftask-1-check-worthiness-estimation)\n* 基于双情感挖掘的虚假新闻检测（Zhang 等，2021）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1903.01728.pdf)\n  [[数据集]](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1pjK0BYiiJt0Ya2nRIrOLCVo-o53sYRBV)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202021-blue)\n* CheckThat! 2020 概述：社交媒体中 claims 的自动识别与验证（Barrón-Cedeño 等，2020）\n  [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-58219-7_17)\n  [[数据集]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fclef2020-checkthat\u002Fdatasets-tools)\n* 需要引用：维基百科可验证性的分类与算法评估（Redi 等，2019）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.11116.pdf)\n  [[数据集]](https:\u002F\u002Ffigshare.com\u002Farticles\u002Fdataset\u002FCitation_Reason_Dataset\u002F7756226)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202019-blue)\n* SemEval-2019 任务 7：RumourEval，确定谣言的真实性及对谣言的支持（Gorrell 等，2019）\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FS19-2147.pdf)\n  [[数据集]](https:\u002F\u002Fcompetitions.codalab.org\u002Fcompetitions\u002F19938)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSemEval%202019-orange)\n* 谣言立场与真实性的联合评估（Lillie 等，2019）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FW19-6122.pdf)\n  [[数据集]](https:\u002F\u002Ffigshare.com\u002Farticles\u002Fdataset\u002FRumourEval_2019_data\u002F8845580)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNoDaLiDa%202019-orange)\n* CLEF-2018 CheckThat! 实验室概述：政治 claims 的自动识别与验证。任务 1：是否值得核查（Atanasova 等，2018）\n  [[论文]](http:\u002F\u002Fceur-ws.org\u002FVol-2125\u002Finvited_paper_13.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fclef2018-factchecking\u002Fclef2018-factchecking\u002F#subtasks)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202017-orange)\n* 区分事实与虚构：用于分类 Twitter 上可疑与可信新闻帖的语言模型（Volkova 等，2017）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FP17-2102.pdf)\n  [[数据集]](https:\u002F\u002Faclanthology.org\u002Fattachments\u002FP17-2102.Datasets.zip)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202017-orange)\n* 政治辩论中检测值得核查 claims 的上下文感知方法（Gencheva 等，2017）\n  [[论文]](https:\u002F\u002Fwww.acl-bg.org\u002Fproceedings\u002F2017\u002FRANLP%202017\u002Fpdf\u002FRANLP037.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fapepa\u002Fclaim-rank)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FRANLP%202017-orange)\n* 使用循环神经网络进行多模态融合以检测微博上的谣言（Jin 等，2017）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3123266.3123454)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACMMM%202017-red)\n* SemEval-2017 任务 8：RumourEval：确定谣言的真实性和对谣言的支持（Derczynski 等，2017）\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FS17-2006.pdf)\n  [[数据集]](https:\u002F\u002Falt.qcri.org\u002Fsemeval2017\u002Ftask8\u002Findex.php?id=data-and-tools)\n* 使用循环神经网络从微博中检测谣言（Ma 等，2016）\n  [[论文]](https:\u002F\u002Fwww.ijcai.org\u002FProceedings\u002F16\u002FPapers\u002F537.pdf)\n  [[数据集]](https:\u002F\u002Fwww.dropbox.com\u002Fs\u002F46r50ctrfa0ur1o\u002Frumdect.zip?dl=0)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202016-purple)\n* 通过对话线程分析人们在社交媒体中如何对待并传播谣言（Zubiaga 等，2016）\n  [[论文]](https:\u002F\u002Fjournals.plos.org\u002Fplosone\u002Farticle?id=10.1371\u002Fjournal.pone.0150989)\n  [[数据集]](https:\u002F\u002Ffigshare.com\u002Farticles\u002FPHEME_rumour_scheme_dataset_journalism_use_case\u002F2068650)\n* CREDBANK：带有相关可信度标注的大规模社交媒体语料库（Mitra 和 Gilbert，2015）\n  [[论文]](http:\u002F\u002Feegilbert.org\u002Fpapers\u002Ficwsm15.credbank.mitra.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fcompsocial\u002FCREDBANK-data)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICWSM%202015-blue)\n* 在总统辩论中检测值得核查的事实 claims （Hassan 等，2015）\n  [[论文]](https:\u002F\u002Fidir.uta.edu\u002F~naeemul\u002Ffile\u002Ffactchecking-cikm15-hassan-cameraready.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCIKM%202015-blue)\n\n### 判决预测数据集\n#### 真实性分类数据集\n##### 自然陈述\n\n* 大型语言模型了解事实吗？（Xu 等，2024）\n  [[论文]](https:\u002F\u002Fopenreview.net\u002Fforum?id=9OevMUdods)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FTHU-BPM\u002FPinocchio)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FTHU-BPM\u002FPinocchio)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICLR%202024-purple)\n* ESCNet：用于多模态事实核查的实体增强与立场检测网络（Zhang 等，2024）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3589334.3645455)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fzfr00\u002FESCNet)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* MCFEND：面向中文假新闻检测的多源基准数据集（Li 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.09092)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FTrustworthyComp\u002Fmcfend)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* 什么使医学声明可（不可）验证？基于实体和关系属性的事实验证分析（Wührl 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.01360)\n  [[数据集]](https:\u002F\u002Fwww.ims.uni-stuttgart.de\u002Fforschung\u002Fressourcen\u002Fkorpora\u002Fbioclaim\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202024-orange)\n* AVeriTeC：包含网络证据的真实世界声明验证数据集（Schlichtkrull 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.13117)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FMichSchli\u002FAVeriTeC)\n  [[共享任务]](https:\u002F\u002Ffever.ai\u002Ftask.html)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNeurIPS%202023-purple)\n* COVID-VTS：短视频平台上的事实提取与验证（Liu 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2302.07919.pdf) \n  [[数据集]](https:\u002F\u002Fgithub.com\u002FFuxiaoLiu\u002FTwitter-Video-dataset) \n  [[代码]](https:\u002F\u002Fgithub.com\u002FFuxiaoLiu\u002FTwitter-Video-dataset) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202023-orange)\n* 端到端多模态事实核查与解释生成：一个具有挑战性的数据集及模型（Yao 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.12487.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FVT-NLP\u002FMocheg)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSIGIR%202023-blue)\n* 基于语义匹配释义建模科学传播中的信息变化（Wright 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.13001.pdf) \n  [[数据集]](https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fcopenlu\u002Fspiced) \n  [[代码]](https:\u002F\u002Fgithub.com\u002Fcopenlu\u002Fscientific-information-change) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202022-orange)\n* 为复杂声明生成字面与隐含子问题以进行事实核查（Chen 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2205.06938.pdf) \n  [[数据集]](https:\u002F\u002Fjifan-chen.github.io\u002FClaimDecomp\u002F) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202022-orange)\n* SciFact-Open：迈向开放域科学声明验证（Wadden 等，2022）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2022.findings-emnlp.347\u002F)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fdwadden\u002Fscifact-open)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202022-orange)\n* CHEF：一个用于循证事实核查的试点中文数据集（Hu 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.11863.pdf) \n  [[数据集]](https:\u002F\u002Fgithub.com\u002FTHU-BPM\u002FCHEF) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNNACL%202022-orange)\n* WatClaimCheck：一个新的声明蕴涵与推理数据集（Khan 等，2022） \n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2022.acl-long.92.pdf) \n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fnxii\u002FWatClaimCheck) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202022-orange)\n* 基于在线资源对脱离上下文图像进行开放域、内容驱动的多模态事实核查（Abdelnabi 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.00061.pdf)\n  [[数据集]](https:\u002F\u002Fs-abdelnabi.github.io\u002FOoC-multi-modal-fc\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCVPR%202022-red)\n* MMM：一种考虑情绪与新颖性的多语言多模态虚假信息检测方法（Gupta 等，2022）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2022.findings-aacl.43.pdf) \n  [[数据集]](https:\u002F\u002Fwww.iitp.ac.in\u002F~ai-nlp-ml\u002Fresources.html#MMM_Dataset) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAACL%202022-orange)\n* FactDrill：用于研究印度假新闻事件的事实核查社交媒体内容数据仓库（Singhal 等，2022）\n  [[论文]](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FICWSM\u002Farticle\u002Fview\u002F19384\u002F19156) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICWSM%202022-blue)\n* 基于证据的健康相关声明事实核查（Sarrouti 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.findings-emnlp.297.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fsarrouti\u002Fhealthver)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202021-orange)\n* COVID-Fact：针对新冠疫情真实世界声明的事实提取与验证（Saakyan 等，2021）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.03794.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fasaakyan\u002Fcovidfact)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* 编辑媒体理解框架：关于视觉虚假信息意图与影响的推理（Da 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.158.pdf)\n  [[代码]](https:\u002F\u002Fjeffda.com\u002Fedited-media-understanding)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* 通过合理化事实核查结构化虚假信息故事（Jiang 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.51.pdf)\n  [[数据集]](https:\u002F\u002Fshanjiang.me\u002Fresources\u002F#fact-check)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* X-FACT：一个新的多语言事实核查基准数据集（Gupta 和 Srikumar，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.acl-short.86.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Futahnlp\u002Fx-fact)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* LUX（语言学方面探究）：用于自动假新闻分类的话语分析（Azevedo 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.findings-acl.4.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Flucas0\u002FLux)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202021-orange)\n* 面向真相：利用客观事实与主观观点实现可解释的谣言检测（Li 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.findings-acl.63.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202021-orange)\n* 公共卫生声明的可解释自动化事实核查（Kotonya 和 Toni，2020b）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.emnlp-main.623.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fneemakot\u002FHealth-Fact-Checking)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* 事实还是虚构：科学声明的验证（Wadden 等，2020）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.emnlp-main.609.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fallenai\u002Fscifact)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* AnswerFact：产品问答中的事实核查（Zhang 等，2020）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.emnlp-main.188.pdf)\n  [[数据集]](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1JAmbLlV0b8Fm03VnNeVEXmROvj1po2lN\u002Fview)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* 公共卫生声明的可解释自动化事实核查（Kotonya 和 Toni，2020）。 \n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.09926)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fneemakot\u002FHealth-Fact-Checking)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* r\u002FFakeddit：一个新的细粒度假新闻检测多模态基准数据集（Nakamura 等，2020）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.lrec-1.755.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fentitize\u002Ffakeddit)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLREC%202020-orange)\n* CLIMATE-FEVER：用于验证现实气候声明的数据集（Diggelmann 等，2020）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.00614.pdf)\n  [[数据集]](https:\u002F\u002Fwww.sustainablefinance.uzh.ch\u002Fen\u002Fresearch\u002Fclimate-fever.html)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTCCML@NeurIPS%202024-purple)\n* FakeCovid——针对COVID-19的多语言跨领域事实核查新闻数据集（Shahi 和 Nandini，2020）。 \n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2006.11343.pdf)\n  [[数据集]](https:\u002F\u002Fgautamshahi.github.io\u002FFakeCovid\u002F)  \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICWSM%202020-blue)\n* FakeNewsNet：包含新闻内容、社交背景及时空信息的数据仓库，用于研究社交媒体上的假新闻（Shu 等，2020）。\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1809.01286.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FKaiDMML\u002FFakeNewsNet)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FBigData%202020-blue)\n* 用于自动化事实核查不同任务的丰富标注语料库（Hanselowski 等，2019）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FK19-1046.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FUKPLab\u002Fconll2019-snopes-crawling)\n  [[数据集]](https:\u002F\u002Ftudatalib.ulb.tu-darmstadt.de\u002Fhandle\u002Ftudatalib\u002F2081)\n  [](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCoNLL%202019-orange)\n* MultiFC：一个现实世界的多领域证据导向声明事实核查数据集（Augenstein 等，2019）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD19-1475.pdf)\n  [[数据集]](https:\u002F\u002Fcompetitions.codalab.org\u002Fcompetitions\u002F21163)\n  [](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202019-orange)\n* 事实核查与伪造摄影：图像相关声明的验证（Zlatkova 等，2019）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FD19-1216.pdf)\n  [[数据集]](https:\u002F\u002Fgitlab.com\u002Fdidizlatkova\u002Ffake-image-detection)\n  [](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202019-orange)\n* FA-KES：围绕叙利亚战争的假新闻数据集（Salem 等，2019）\n  [[论文]](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FICWSM\u002Farticle\u002Fview\u002F3254\u002F3122)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Ffakenewssyria\u002Ffake_news_detection)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICWSM%202019-blue)\n* 社区论坛中的事实核查（Mihaylova 等，2018）\n  [[论文]](https:\u002F\u002Fwww.aaai.org\u002Focs\u002Findex.php\u002FAAAI\u002FAAAI18\u002Fpaper\u002Fview\u002F16780\u002F16082)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fqcri\u002FQLFactChecking)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202018-purple)\n* EANN：用于多模态假新闻检测的事件对抗神经网络\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3219819.3219903)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fyaqingwang\u002FEANN-KDD18)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FKDD%202018-blue)\n* CLEF-2018 CheckThat! 实验室关于政治声明自动识别与验证的概述。任务2：真实性  （Barrón-Cedeño 等，2018）\n  [[论文]](http:\u002F\u002Fceur-ws.org\u002FVol-2125\u002Finvited_paper_14.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fclef2018-factchecking\u002Fclef2018-factchecking\u002F#subtasks)\n* 将立场检测与事实核查整合到统一语料库中（Baly 等，2018）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FN18-2004.pdf)\n  [[数据集]](https:\u002F\u002Falt.qcri.org\u002Fresources\u002Farabic-fact-checking-and-stance-detection-corpus\u002F)\n  [](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202018-orange)\n* 对极端党派性和假新闻的文体学探究（Potthast 等，2018）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FP18-1022.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fwebis-de\u002FACL-18)\n  [](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202018-orange)\n* 包含Facebook用户评论与个人视角的新闻真实性数据集（Santia 和 Williams，2018）\n  [[论文]](https:\u002F\u002Faaai.org\u002Focs\u002Findex.php\u002FICWSM\u002FICWSM18\u002Fpaper\u002Fview\u002F17825\u002F17046)]\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fgsantia\u002FBuzzFace)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICWSM%202018-blue)\n* 抽样新闻生产者：用于研究复杂媒体格局的大型新闻与专题数据集（Horne 等，2018）\n  [[论文]](https:\u002F\u002Faaai.org\u002Focs\u002Findex.php\u002FICWSM\u002FICWSM18\u002Fpaper\u002Fview\u002F17796\u002F17044)\n  [[数据集]](https:\u002F\u002Fdataverse.harvard.edu\u002Fdataset.xhtml?persistentId=doi:10.7910\u002FDVN\u002FZCXSKG)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICWSM%202018-blue)\n* 不同层次的真实性：假新闻与政治事实核查中的语言分析（Rashkin 等，2017）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD17-1317.pdf)\n  [[数据集]](https:\u002F\u002Fhrashkin.github.io\u002Ffactcheck.html)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202017-orange)\n* “说谎者，说谎者，裤子着火了”：一个新的假新闻检测基准数据集（Wang，2017）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP17-2067.pdf)\n  [[数据集]](https:\u002F\u002Fsites.cs.ucsb.edu\u002F~william\u002Fsoftware.html)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202017-orange)\n* 网络文本声明的可信度评估（Popat 等，2016）\n  [[论文]](http:\u002F\u002Fresources.mpi-inf.mpg.de\u002Fimpact\u002Fweb_credibility_analysis\u002Fcikm2016-popat.pdf)\n  [[数据集]](https:\u002F\u002Fwww.mpi-inf.mpg.de\u002Fdepartments\u002Fdatabases-and-information-systems\u002Fresearch\u002Fimpact\u002Fweb-credibility-analysis)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCIKM%202016-blue)\n* Emergent：一个新的立场分类数据集（Ferreira 和 Vlachos，2016）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FN16-1138.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fwillferreira\u002Fmscproject)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202016-orange)\n* 假新闻还是真相？利用讽刺线索检测潜在误导性新闻（Rubin 等，2016）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FW16-0802.pdf) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCADD@ACL%202016-orange)\n* 简单统计属性相关声明的识别与验证（Vlachos 和 Riedel，2015）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FD15-1312.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fuclnlp\u002FsimpleNumericalFactChecker)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202015-orange)\n* 质量检查：任务定义与数据集构建（Vlachos 和 Riedel，2014）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FW14-2508.pdf)\n  [[数据集]](https:\u002F\u002Fwww.dropbox.com\u002Fs\u002Fuvwbpjytogqnm68\u002FFactChecking_LTCSS2014_release.ods?dl=0)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLTCSS@ACL%202014-orange)\n* 民事与刑事叙事中基于语言的欺骗指标的验证与实施（Bachenko 等，2008）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FC08-1006.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCOLING%202008-orange)\n\n##### 人工标注数据集\n* EX-FEVER：用于多跳可解释事实核查的数据集（Ma 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2310.09754)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fdependentsign\u002FEX-FEVER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* CFEVER：中文事实提取与验证数据集（Lin 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.13025)\n  [[数据集]](https:\u002F\u002Fikmlab.github.io\u002FCFEVER\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* FACTKG：基于知识图谱推理的事实核查（Kim 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.06590.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fjiho283\u002FFactKG)\n  [[数据集]](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1q0_MqBeGAp5_cBJCBf_1alYaYm14OeTk?usp=share_link)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* 为真实假新闻检测而伪造假新闻：宣传导向训练数据生成（Huang 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.05386.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fkhuangaf\u002FFakingFakeNews)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fkhuangaf\u002FFakingFakeNews)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* FACTIFY-5WQA：基于 5W 要素的问题回答式事实核查（Rani 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.04329.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* 基于图表图像的阅读与推理用于证据驱动的自动化事实核查（Akhtar 等，2023）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2023.findings-eacl.30.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fmubasharaak\u002FChartFC_chartBERT)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202023-orange)\n* 虚假信息反应框架：关于读者对新闻标题反应的推理（Gabriel 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.08790.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fskgabriel\u002Fmrf-modeling)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202022-orange)\n* DialFact：对话中事实核查的基准测试（Gupta 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.08222.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fsalesforce\u002FDialFact)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202022-orange)\n* FAVIQ：从信息查询问题进行事实核查（Park 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.05707)\n  [[数据集]](https:\u002F\u002Ffaviq.github.io\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202022-orange)\n* FEVEROUS：面向非结构化与结构化信息的事实提取与验证（Aly 等，2021）  \n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2106.05707)\n  [[数据集]](https:\u002F\u002Ffever.ai\u002Fresources.html)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FRaldir\u002FFEVEROUS)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNeurIPS%202021-purple)\n* 使用表格进行陈述验证与证据发现（SEM-TAB-FACT）（Wang 等，2021）\n  [[数据集]](https:\u002F\u002Fcompetitions.codalab.org\u002Fcompetitions\u002F27748)\n* 补充你的维生素 C！利用对比证据进行稳健的事实核查（Schuster 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.naacl-main.52.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FTalSchuster\u002FVitaminC)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202021-orange)\n* ParsFEVER：波斯语事实提取与验证数据集（Zarharan 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.starsem-1.9.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FZarharan\u002FParsFEVER)\n* DanFEVER：丹麦语声明验证数据集（Nørregaard 和 Derczynski，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.nodalida-main.47.pdf)\n  [[数据集]](https:\u002F\u002Ffigshare.com\u002Farticles\u002Fdataset\u002FDanFEVER_claim_verification_dataset_for_Danish\u002F14380970)\n* HoVer：用于多跳事实提取与声明验证的数据集（Jiang 等，2020）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.findings-emnlp.309.pdf)\n  [[数据集]](https:\u002F\u002Fhover-nlp.github.io\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202020-orange)\n* INFOTABS：将表格作为半结构化数据进行推理（Gupta 等，2020）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.acl-main.210.pdf)\n  [[数据集]](https:\u002F\u002Faclanthology.org\u002Fattachments\u002F2020.acl-main.210.Dataset.zip)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202020-orange)\n* TabFact：大规模基于表格的事实核查数据集（Chen 等，2020）\n  [[论文]](https:\u002F\u002Fopenreview.net\u002Fpdf?id=rkeJRhNYDH)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fwenhuchen\u002FTable-Fact-Checking)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICLR%202020-purple)\n* 基于知识图谱中正负证据路径加权的无监督事实核查（Kim 和 Choi，2020）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.coling-main.147.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCOLING%202020-orange)\n* 立场预测与声明验证：阿拉伯视角（Khouja，2020）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.fever-1.2.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Flatynt\u002Fans)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@ACL%202020-orange)\n* 自动核查维基百科中的声明（Sathe 等，2020）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.lrec-1.849.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fwikifactcheck-english\u002Fwikifactcheck-english)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLREC%202020-orange)\n* FEVER：大规模事实提取与验证数据集（Thorne 等，2018）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FN18-1074.pdf)\n  [[数据集]](https:\u002F\u002Ffever.ai\u002Fresources.html)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202018-orange)\n* 自动检测虚假新闻（Pérez-Rosas 等，2018）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FC18-1287.pdf)\n  [[数据集]](https:\u002F\u002Flit.eecs.umich.edu\u002Fdownloads.html#undefined)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCOLING%202018-orange)\n* 撒谎检测器：自动识别欺骗性语言的探索（Mihalcea 和 Strapparava，2009）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FP09-2078.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202009-orange)\n* 在知识图谱中寻找支持事实核查的路径（Shiralkar 等，2017）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1708.07239.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fshiralkarprashant\u002Fknowledgestream)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICDM%202017-blue)\n* 面向知识图谱事实核查的判别式谓词路径挖掘（Shi 和 Weninger，2016）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1510.05911)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FKBS%202016-blue)\n* 基于知识网络的计算事实核查（Ciampaglia 等，2015）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1501.03471.pdf)\n\n#### 操控分类数据集\n* “图片，告诉我你的故事！”预测视觉虚假信息的原始元上下文（Tonglet 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2408.09939)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FUKPLab\u002F5pils)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* 跨领域音频深度伪造检测：数据集与分析（Li 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2404.04904)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fleolya\u002FCD-ADD)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* DF-Platter：多人脸异构深度伪造数据集（Narayan 等，2023）\n  [[论文]](https:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent\u002FCVPR2023\u002Fpapers\u002FNarayan_DF-Platter_Multi-Face_Heterogeneous_Deepfake_Dataset_CVPR_2023_paper.pdf)\n  [[数据集]](https:\u002F\u002Fiab-rubric.org\u002Fdf-platter-database)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCVPR%202023-red)\n* 检测与定位多模态媒体操纵。（Shao 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2304.02556.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Frshaojimmy\u002FMultiModal-DeepFake)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCVPR%202023-red)\n* FakeAVCeleb：一种新型音视频多模态深度伪造数据集（Khalid 等，2021）\n  [[论文]](https:\u002F\u002Fdatasets-benchmarks-proceedings.neurips.cc\u002Fpaper_files\u002Fpaper\u002F2021\u002Ffile\u002Fd9d4f495e875a2e075a1a4a6e1b9770f-Paper-round2.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FDASH-Lab\u002FFakeAVCeleb)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNeurIPS%202021-purple)\n* 半真半假：部分伪造音频检测数据集（Yi 等，2021）\n  [[论文]](https:\u002F\u002Fwww.isca-archive.org\u002Finterspeech_2021\u002Fyi21_interspeech.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FInterspeech%202019-green)\n* KoDF：大规模韩语深度伪造检测数据集（Kwon 等，2021）\n  [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9710066)\n  [[数据集]](https:\u002F\u002Fmoneybrain-research.github.io\u002Fkodf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICCV%202021-red)\n* Celeb-DF：用于深度伪造取证的大规模挑战性数据集（Li 等，2020）\n  [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9156368)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fyuezunli\u002Fceleb-deepfakeforensics)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCVPR%202020-red)\n* DeeperForensics-1.0：用于现实世界人脸伪造检测的大规模数据集（Jiang 等，2020）\n  [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=9156686)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FEndlessSora\u002FDeeperForensics-1.0)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCVPR%202020-red)\n* DeepSonar：迈向高效且稳健的AI合成虚假语音检测（Wang 等，2020）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3394171.3413716)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACMMM%202020-red)\n* FoR：用于合成语音检测的数据集（Reimao 等，2019）\n  [[论文]](https:\u002F\u002Fbil.eecs.yorku.ca\u002Fwp-content\u002Fuploads\u002F2020\u002F01\u002FFoR-Dataset_RR_VT_final.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSpeD%202019-green)\n* Phonespoof：用于电话信道欺骗攻击检测的新数据集（Lavrentyeva 等，2019）\n  [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=8682942)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICASSP%202019-green)\n* 深度伪造检测挑战赛（DFDC）预览数据集（Dolhansky 等，2019）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1910.08854.pdf)\n  [[数据集]](https:\u002F\u002Fdeepfakedetectionchallenge.ai\u002F)\n* PS-Battles 数据集——用于图像操纵检测的图像集合（Heller 等，2018）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1804.04866.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FdbisUnibas\u002FPS-Battles)\n* FaceForensics：用于人脸伪造检测的大规模视频数据集（Rossler 等，2018）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1803.09179.pdf)\n  [[数据集]](https:\u002F\u002Fjustusthies.github.io\u002Fposts\u002Ffaceforensics\u002F)\n\n\n#### 脱离上下文分类数据集\n* 并非所有假新闻都是文字形式：误导性视频标题的数据集与分析（Sung 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2310.13859.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fyysung\u002FVMH\u002Ftree\u002Fmaster)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202023-orange)\n* COSMOS：利用自监督学习捕捉脱离上下文的虚假信息（Aneja 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2101.06278.pdf)\n  [[代码]](https:\u002F\u002Fshivangi-aneja.github.io\u002Fprojects\u002Fcosmos\u002F)\n  [[数据集]](https:\u002F\u002Fdocs.google.com\u002Fforms\u002Fd\u002F13kJQ2wlv7sxyXoaM1Ddon6Nq7dUJY_oftl-6xzwTGow\u002Fviewform?edit_requested=true)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* Factify 2：多模态假新闻与讽刺新闻数据集（Suryavardan 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fftp\u002Farxiv\u002Fpapers\u002F2304\u002F2304.03897.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fsurya1701\u002FFactify-2.0)\n* InfoSurgeon：跨媒体细粒度信息一致性检查用于假新闻检测（Fung 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.133\u002F)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fyrf1\u002FInfoSurgeon)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* NewsCLIPpings：自动生成功能脱离上下文的多模态媒体（Luo 等，2021）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.05893.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fg-luo\u002Fnews_clippings)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* 检测跨模态不一致以防御神经网络生成的假新闻（Tan 等，2020）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2009.07698.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Frxtan2\u002FDIDAN)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* 利用跨模态实体一致性指标对真实世界新闻进行多模态分析（Müller-Budack 等，2020）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3372278.3390670)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FTIBHannover\u002Fcross-modal_entity_consistency)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICMR%202020-red)\n* 深度多模态图像再利用检测（Sabir 等，2018）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1808.06686.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FEkraam\u002FMEIR)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACMMM%202018-red)\n* 利用图像与文本联合嵌入评估多媒体语义完整性（Jaiswal 等，2017）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3123266.3123385)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACMMM%202017-red)\n\n## 共享任务\n* AVeriTec 共享任务 [[第7届 FEVER 研讨会](https:\u002F\u002Ffever.ai\u002Ftask.html)]\n* 事实提取与验证（FEVER）共享任务 [[第5届 FEVER 研讨会](https:\u002F\u002Ffever.ai\u002F)]\n* 带有表格的语句验证与证据查找（SEM-TAB-FACT）[[Wang 等，2021年](https:\u002F\u002Fcompetitions.codalab.org\u002Fcompetitions\u002F27748)] \n* SciFact 主张验证 [[Wadden 等，2020年](https:\u002F\u002Fsdproc.org\u002F2021\u002Fsharedtasks.html#sciver)]\n* Fakeddit 多模态假新闻检测挑战赛 [[Nakamura 等，2020年](https:\u002F\u002Fcompetitions.codalab.org\u002Fcompetitions\u002F25337#learn_the_details)]\n* SemEval-2019 任务7：RumourEval，确定谣言的真实性及对谣言的支持度 [[Gorrell 等，2019年](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FS19-2147\u002F)]\n* SemEval-2019 任务8：社区问答论坛中的事实核查 [[Mihaylova 等，2019年](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FS19-2149\u002F)]\n* 对假新闻挑战赛立场检测任务的回顾性分析 [[Hanselowski 等，2018年](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FC18-1158\u002F)]\n* 事实提取与验证（FEVER）共享任务 [[Thorne 等，2018年](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FW18-5501\u002F)]\n* SemEval-2017 任务8：RumourEval：确定谣言的真实性及对谣言的支持度 [[Derczynski 等，2017年](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FS17-2006\u002F)]\n* 假新闻挑战赛（FNC-1）[[Pomerleau 和 Rao，2017年](http:\u002F\u002Fwww.fakenewschallenge.org\u002F)]\n\n\n## 模型\n\n### 声称检测与提取\n* 破解谣言：一种意图感知的层次化对比学习多任务学习方法（Yang等，2024）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.256.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* 基于大语言模型增强语义挖掘的假新闻检测（Ma等，2024）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.31.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* 面向事实核查的文档级声称提取与去情境化（Deng等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.03239)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202024-orange)\n* 基于强化微调的大语言模型联合立场检测与谣言辟谣（Yang等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.02143)\n  [[代码]](https:\u002F\u002Fanonymous.4open.science\u002Fr\u002FJSDRV-F3CE\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* 通过提示与扩散揭示观点演变以进行短视频假新闻检测（Zong等，2024）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2024.findings-acl.642.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* 从怀疑到接受：模拟对假新闻的态度动态（Liu等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.09498)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202024-purple)\n* 用于假新闻检测的异构子图Transformer（Zhang等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2404.13192)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* 语义演化增强的图自编码器用于谣言检测（Tao等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2404.16076)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* T\u003Csup>3\u003C\u002Fsup>RD：社交媒体上谣言检测的测试时训练（Zhang等，2024）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3589334.3645443)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fsocial-rumors\u002FT3RD)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* 结合合成过采样的双图网络用于社交媒体上的不平衡谣言检测（Lu等，2024）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3589335.3651494)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* 基于深度强化学习的社会网络谣言缓解（Su等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.09217)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* 适应大语言模型时代的假新闻检测（Su等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2311.04917)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fmbzuai-nlp\u002FFakenews-dataset)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* 新闻媒体来源画像的交互式框架（Mehta等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2309.07384)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fhockeybro12\u002FInteractive_News_Media_Profiling)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* CMA-R：用于解释谣言检测的因果中介分析（Tian等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.08155)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fltian678\u002Fcma-r)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202024-orange)\n* Style-News：结合风格化新闻生成与对抗验证的神经网络假新闻检测（Wang等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2401.15509)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202024-orange)\n* 用于多模态假新闻检测的强化自适应知识学习（Zhang等，2024）\n  [[论文]](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F29618)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* 基于神经符号推理揭示多模态假新闻中的隐性欺骗模式（Dong等，2024）\n  [[论文]](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F28677)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fhedongxiao-tju\u002FNSLM)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* 传播树并不深：用于谣言检测的自适应图对比学习方法（Cui等，2024）\n  [[论文]](https:\u002F\u002Fojs.aaai.org\u002Findex.php\u002FAAAI\u002Farticle\u002Fview\u002F27757)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* 频谱在多模态表征与融合中更有效：一种多模态频谱谣言检测器（Lao等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2312.11023)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fdm4m\u002FFSRU)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* GAMC：一种基于掩码图自编码器的无监督假新闻检测方法（Yin等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2312.05739)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* 利用网络效应缓解假新闻：通过自我模仿学习选择辟谣者（Xu等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.03357)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fxxfwin\u002FNAGASIL)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* 恶意行为者，良师益友：探索大语言模型在假新闻检测中的作用（Hu等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2309.12247)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FICTMCG\u002FARG)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202024-purple)\n* 基于逻辑推理的可解释多模态虚假信息检测（Liu等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.05964.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fless-and-less-bugs\u002FLogicMD)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* 两个脑袋胜过一个：通过与邻居相关联提升假新闻视频检测效果（Qi等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.05241.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FICTMCG\u002FNEED)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* 从过去学习，为未来进化：预测时间趋势以辅助假新闻检测（Hu等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.14728.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FICTMCG\u002FFTT-ACL23)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* 多模态假新闻检测中的因果干预与反事实推理（Chen等，2023）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2023.acl-long.37.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* MetaAdapt：基于元学习的领域自适应少样本虚假信息检测（Yue等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.12692.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FYueeeeeeee\u002FMetaAdapt)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* 基于传播结构的零样本谣言检测（Lin等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.01117.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* 基于对比学习和交叉注意力的无监督跨域谣言检测（Ran等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.11945.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* 拉远视角观察：面向假新闻检测的新闻环境感知（Sheng等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.10885.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FICTMCG\u002FNews-Environment-Perception\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202022-orange)\n* DDGCN：用于社交媒体上谣言检测的双动态图卷积网络（Sun等，2022）\n  [[论文]](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-6370.SunM.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202022-purple)\n* 基于声明引导的层次化图注意力网络在Twitter上的谣言检测（Lin等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.786.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* STANKER：基于层级粒度注意力掩码BERT的堆叠网络用于社交媒体上的谣言检测（Rao等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.269.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Ffip-lab\u002FSTANKER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* 不一致性很重要：一种知识引导的双不一致性网络用于多模态谣言检测（Sun等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.findings-emnlp.122.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FMengzSun\u002Fdual-inconsistency-rumor-detection-network)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202021-orange)\n* 社交媒体上谣言识别的主动学习（Farinneya等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.findings-emnlp.387.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202021-orange)\n* 朝着传播不确定性迈进：边缘增强的贝叶斯图卷积网络用于谣言检测（Wei等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.297.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fweilingwei96\u002FEBGCN)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* 具有对抗意识的谣言检测（Song等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.findings-acl.118.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fyunzhusong\u002FAARD)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202021-orange)\n* 学习解耦潜在主题以对Twitter上的谣言真实性进行分类（Dougrez-Lewis等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.findings-acl.341.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FJohnNLP\u002FSAVED)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202021-orange)\n* 为假新闻检测挖掘双重情感（Zhang等，2021）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3442381.3450004)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FRMSnow\u002FWWW2021)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202021-blue)\n* 将值得核查的声明检测视为正类未标注学习（Wright和Augenstein，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.findings-emnlp.43.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fcopenlu\u002Fcheck-worthiness-pu-learning)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202020-orange)\n* 利用微博对话结构检测谣言（Li等，2020）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.coling-main.473.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCOLING%202018-orange)\n* 使用树形Transformer在Twitter上辟谣（Ma等，2020）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.coling-main.476.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCOLING%202018-orange)\n* VRoC：基于文本的变分自编码器辅助多任务谣言分类器（Cheng等，2020）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.00816.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fcmxxx\u002FVRoC)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202020-blue)\n* 基于图结构对抗学习的社交媒体谣言检测（Yang等，2020）\n  [[论文]](https:\u002F\u002Fijcai.org\u002Fproceedings\u002F2020\u002F0197.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202020-purple)\n* 通过关注用户互动实现微博中可解释的谣言检测（Khoo等，2020）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2001.10667.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fserenaklm\u002Frumor_detection)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202020-purple)\n* 基于双向图卷积网络的社交媒体谣言检测（Bian等，2020）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2001.06362.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FTianBian95\u002FBiGCN)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202020-purple)\n* 假新闻早期检测：一种理论驱动的模型（Zhou等，2020）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3377478)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202020-purple)\n* MVAE：用于假新闻检测的多模态变分自编码器（Khattar等，2019）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3308558.3313552)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fdhruvkhattar\u002FMVAE)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202019-blue)\n* 使用几何深度学习检测社交媒体上的假新闻（Monti等，2019）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1902.06673.pdf)\n* 基于树状递归神经网络在Twitter上检测谣言（Ma等，2018）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FP18-1184.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FmajingCUHK\u002FRumor_RvNN)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202018-orange)\n* 基于层次社交注意力网络的谣言检测（Guo等，2018）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3269206.3271709)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCIKM%202017-blue)\n* 一种结合启发式方法和监督学习的混合识别系统，用于检测值得核查的声明（Zuo等，2018）\n  [[论文]](http:\u002F\u002Fceur-ws.org\u002FVol-2125\u002Fpaper_143.pdf)\n* 用于谣言分析的简单开放式立场分类（Aker等，2017）\n  [[论文]](https:\u002F\u002Fwww.acl-bg.org\u002Fproceedings\u002F2017\u002FRANLP%202017\u002Fpdf\u002FRANLP005.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FRANLP%202017-orange)\n* NileTMRG参加SemEval-2017任务8：确定Twitter上谣言及其真实性支持（Enayet和El-Beltagy，2017）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FS17-2082.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSemEval@ACL%202017-orange)\n* Turing参加SemEval-2017任务8：使用分支LSTM的顺序方法进行谣言立场分类（Kochkina等，2017）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FS17-2083.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSemEval@ACL%202017-orange)\n* 自动识别热门Twitter话题中的假新闻（Buntain和Golbeck，2017）\n  [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8118443)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSmartCloud%202017-blue)\n* 使用循环神经网络从微博中检测谣言（Ma等，2016）\n  [[论文]](https:\u002F\u002Fijcai.org\u002FProceedings\u002F16\u002FPapers\u002F537.pdf)\n  [[数据集]](https:\u002F\u002Fwww.dropbox.com\u002Fs\u002F46r50ctrfa0ur1o\u002Frumdect.zip?dl=0)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202016-purple)\n\n### 判决预测\n#### 真实性分类\n* 我们需要针对特定语言的事实核查模型吗？以中文为例（张等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2401.15498)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fcaiqizh\u002FFC_Chinese)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n\n* FinDVer: 面向长篇且混合内容金融文档的可解释性事实核查（Zhao 等，2024）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.818.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fyilunzhao\u002FFinDVer)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* MiniCheck: 基于基础文档的高效大语言模型事实核查（Tang 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2404.10774)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FLiyan06\u002FMiniCheck)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* 基于多阶段重排序的事实核查证据检索（Malviya 等，2024）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2024.findings-emnlp.428.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202024-orange)\n* ChartCheck: 面向真实图表图像的可解释性事实核查（Akhtar 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2311.07453)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fmubasharaak\u002FChartCheck)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* 证据检索几乎是事实核查的全部所需（Zheng 等，2024）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2024.findings-acl.551.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* 通过合成对比性论据进行检索增强的事实核查（Yue 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2406.09815)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fyueeeeeeee\u002FRAFTS)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202024-orange)\n* MetaSumPerceiver: 面向事实核查的多模态多文档证据摘要（等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2407.13089)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202024-orange)\n* VeraCT Scan: 基于检索的、具有可解释推理的假新闻检测（Niu 等，2024）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2024.acl-demos.25.pdf)\n  [[演示]](https:\u002F\u002Fveractscan.newsbreak.com\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDemo@ACL%202024-orange)\n* Event-Radar: 事件驱动的多视角学习用于多模态假新闻检测（等，2024）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2024.acl-long.316.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202024-orange)\n* 用于假新闻检测的统一证据增强推理框架（Wu 等，2024）\n  [[论文]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F0723.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202024-purple)\n* 以自然语言为中心的推理网络用于多模态假新闻检测（Zhang 等，2024）\n  [[论文]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2024\u002F0281.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202024-purple)\n* 从生成到澄清：ChatGPT 在假新闻泥潭中的历程（Huang 等，2024）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3589335.3651509)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* MSynFD: 多跳语法感知的假新闻检测（Liang 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.14834)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* 应对新发危机中的假新闻：以新冠肺炎为例（Yang 等，2024）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3589335.3651506)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FDSAIL-SKKU\u002FFighting_Against_FakeNews_on_Emerging_Crisis-WWW24)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* Self-Checker: 用于大型语言模型事实核查的即插即用模块（Li 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.14623)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* 超出训练集范围的事实核查（Karisani 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.18671)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fp-karisani\u002FOODFC)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* 语言模型会幻觉，但可能擅长事实核查（Guan 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2310.14564)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FJianGuanTHU\u002FLLMforFV)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* 使用野外检索证据进行复杂主张核查（Chen 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.11859)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fjifan-chen\u002FFact-checking-via-Raw-Evidence)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* MAPLE: 少样本主张核查的成对语言演化微观分析（Zeng 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2401.16282)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FXiaZeng0223\u002FMAPLE)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202024-orange)\n* 重新思考事实核查的损失函数（Mukobara 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.08174)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fyuta-mukobara\u002FRLF-KGAT)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202024-orange)\n* 对比知识源用于开放域科学主张核查（Vladika 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.02844)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fjvladika\u002FComparing-Knowledge-Sources)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202024-orange)\n* 因果漫步：利用前门调整去偏多跳事实核查（Zhang 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.02698)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fzcccccz\u002FCausalWalk)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* 面向文本和表格的事实核查的异构图推理（Gong 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.13028)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FDeno-V\u002FHeterFC)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* 对话中的自动化事实核查：是否需要专用模型？（Chamoun 等，2023）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2023.emnlp-main.993.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202023-orange)\n* DECKER: 利用异质知识双重检查常识性事实核查（Zou 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.05921.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FAnni-Zou\u002FDecker)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Check-COVID: 利用科学证据核查新冠肺炎新闻主张（Wang 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.18265.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fposuer\u002FCheck-COVID)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* Claim-Dissector: 具有联合重排序和真实性预测的可解释事实核查系统（Fajcik 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2207.14116.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FKNOT-FIT-BUT\u002FClaimDissector)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* 提示一致性优于自我一致性？基于预训练语言模型的少样本和零样本事实核查（Zeng 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2306.02569.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fznhy1024\u002FProToCo)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* 反事实去偏用于事实核查（Xu 等，2023）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2023.acl-long.374.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* 利用程序引导推理核查复杂主张（Pan 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.12744.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fmbzuai-nlp\u002FProgramFC)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* 自举式多视图表示用于假新闻检测（Ying 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2206.05741.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* 变焦问题生成用于事实核查（Ousidhoum 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2210.12400.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202022-orange)\n* ProoFVer: 基于自然逻辑定理证明的事实核查（Krishna 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.11357.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTACL%202022-orange)\n* MultiVerS: 利用弱监督和全文上下文改进科学主张核查（Wadden 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2112.01640.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fdwadden\u002Fmultivers)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@NAACL%202022-orange)\n* 为零样本科学事实核查生成科学主张（Wright 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.12990.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fallenai\u002Fscientific-claim-generation)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202022-orange)\n* 利用事实知识自动检测实体操纵文本（Jawahar 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2203.12990.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FUBC-NLP\u002Fmanipulated_entity_detection)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202022-orange)\n* LOREN: 用于可解释事实核查的逻辑正则化推理（Chen 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2012.13577.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fjiangjiechen\u002FLOREN?ref=pythonrepo.com)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202022-purple)\n* 向细粒度推理迈进：用于假新闻检测（Jin 等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2110.15064.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202022-purple)\n* 针对自动化事实核查系统的合成虚假信息攻击（Du 等，2021）\n  [[论文]](https:\u002F\u002Fwww.aaai.org\u002FAAAI22Papers\u002FAAAI-11986.DuY.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FYibing-Du\u002Fadversarial-factcheck)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202022-purple)\n* 编辑语言模型中的事实知识（De Cao 等，2021）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2104.08164.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fnicola-decao\u002FKnowledgeEditor)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* 面向基于表格的事实核查的逻辑级证据检索与基于图的验证网络（Shi 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.16.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fqshi95\u002FLERGV)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* 一起学习的学生学得更好：关于集体知识蒸馏在事实核查领域迁移中的重要性（Mithun 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.558.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* 摘要、理由、立场：用于科学主张核查的联合模型（Zhang 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.emnlp-main.290.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FZhiweiZhang97\u002FARSJointModel)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202021-orange)\n* 基于表格的事实核查与显著性感知学习（Wang 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.findings-emnlp.338.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fluka-group\u002FSalience-aware-Learning)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202021-orange)\n* 探索分解法用于基于表格的事实核查（Yang 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.findings-emnlp.90.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Farielsho\u002Fdecomposition-table-reasoning)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202021-orange)\n* 面向表格的开放式事实核查的联合验证与重排序（Schlichtkrull 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.529.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FOpenTableFactChecking)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* 面向知识密集型任务的多任务检索（Maillard 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.89.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* 面向事实核查的主题感知证据推理与立场感知聚合（Si 等，2021）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.01191.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fjasenchn\u002FTARSA)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* 基于DQN的方法寻找精确证据用于事实核查（Wan 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.83.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fsysulic\u002FDQN-FV)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* 统一双重视角认知模型用于可解释主张核查（Wu 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.5.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* 与知识比较：基于外部知识的图神经网络假新闻检测（Hu 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.62.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FBUPT-GAMMA\u002FCompareNet_FakeNewsDetection)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* 自动假新闻检测：模型是否正在学习推理？（Hansen 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.acl-short.12.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fcasperhansen\u002Ffake-news-reasoning)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* 探索使用T5进行列表式证据推理用于事实核查（Jiang 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.acl-short.51.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* 多模态融合与协同注意力网络用于假新闻检测（Wu 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.findings-acl.226.pdf)  \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202021-orange)\n* 用于基于证据的事实核查的多级注意力模型（Kruengkrai 等，2021）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2106.00950.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fnii-yamagishilab\u002Fmla)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202021-orange)\n* 强大而轻量的基线模型用于事实核查联合推理（Tymoshenko 等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.findings-acl.426.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fikernels\u002Freasoning-baselines)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202021-orange)\n* 面向知识密集型NLP任务的检索增强生成（Lewis 等，2020）。\n  [[论文]](https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Ffile\u002F6b493230205f780e1bc26945df7481e5-Paper.pdf)\n  [[代码]](https:\u002F\u002Fhuggingface.co\u002Ftransformers\u002Fmodel_doc\u002Frag.html)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNeurIPS%202021-purple)\n* 语言模型能充当事实核查员吗？（Lee 等，2020）。\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.fever-1.5.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@ACL%202020-orange)\n* 用于自动化事实提取和核查的层次化证据集建模（Subramanian 等，2020）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2010.05111.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FShyamSubramanian\u002FHESM)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* 利用言语化和图注意力网络进行程序增强的事实核查（Yang 等，2020）。\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.emnlp-main.628.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Farielsho\u002FProgram-Enhanced-Table-Fact-Checking)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* 通过中间预训练理解表格（Eisenschlos 等，2020）。\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.findings-emnlp.27.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fgoogle-research\u002Ftapas) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202020-orange)\n* 利用内核图注意力网络进行细粒度事实核查（Liu 等，2020）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.acl-main.655.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fthunlp\u002FKernelGAT)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202020-orange)\n* 基于语义级图进行事实核查的推理（Zhong 等，2020）。\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.acl-main.549.pdf)\n* LogicalFactChecker: 利用逻辑运算结合图模块网络进行事实核查（Zhong 等，2020）。\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.acl-main.539.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202020-orange)\n* Scrutinizer: 一种混合式大规模数据驱动主张核查方法（Karagiannis 等，2020） \n  [[论文]](http:\u002F\u002Fwww.vldb.org\u002Fpvldb\u002Fvol13\u002Fp2508-karagiannis.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fgeokaragiannis\u002Fstatchecker) \n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FVLDB%202020-blue)\n* 无监督问答用于事实核查（Jobanputra，2019）。\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FD19-6609.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fmayankjobanputra\u002FUQA-fever)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202019-orange)\n* GEAR: 基于图的证据聚合与推理用于事实核查（Zhou 等，2019）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FP19-1085.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fthunlp\u002FGEAR)]\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202019-orange)\n* 结合事实提取与核查的神经语义匹配网络（Nie 等，2019）。\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1811.07039.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Feasonnie\u002Fcombine-FEVER-NSMN\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202019-orange)\n* Team DOMLIN: 利用证据增强参加FEVER共享任务（Stammbach和Neumann，2019）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FD19-6616.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fnecla-ml\u002Ffever2018)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202019-orange)\n* 句子级证据嵌入用于主张核查与层次化注意力网络（Ma 等，2019）。\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FP19-1244.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202019-orange)\n* BERT用于证据检索和主张核查（Soleimani等，2019）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1910.02655.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FASoleimaniB\u002FBERT_FEVER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FECIR%202019-blue)\n* TwoWingOS: 一种双翼优化策略用于证据主张核查（Yin和Roth，2018）。\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FD18-1010.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fyinwenpeng\u002FFEVER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202018-orange)\n* UKP-Athene: 多句文本蕴含用于主张核查（Hanselowski等，2018）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FW18-5516.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FUKPLab\u002Ffever-2018-team-athene)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202018-orange)\n* Team Papelo: FEVER中的Transformer网络（Malon，2018）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FW18-5517.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fnecla-ml\u002Ffever2018)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202018-orange)\n* QED: 一个用于FEVER共享任务的事实核查系统（Luken等，2018）。\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FW18-5526.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fjluken\u002FFEVER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202018-orange)\n* UCL机器阅读小组：四因素框架用于事实发现（HexaF）（Yoneda等，2018）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FW18-5515.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fuclmr\u002Ffever)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202018-orange)\n* 仅凭谣言立场能否预测真实性？（Dungs等，2018）。\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FC18-1284.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCOLING%202018-orange)\n* 不同色调：分析假新闻与政治事实核查中的语言（Rashkin等，2017）。\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FD17-1317.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202017-orange)\n\n#### 操控分类\n*\n  [[论文]]()\n  [[数据集]]()\n  ****\n\n#### 脱离上下文分类\n* 基于多模态大语言模型从合成数据中学习的多模态虚假信息检测（Zeng 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2409.19656)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202024-orange)\n* SNIFFER：用于可解释脱离上下文虚假信息检测的多模态大型语言模型（Qi 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.03170)\n  [[数据集]](https:\u002F\u002Fpengqi.site\u002FSniffer\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FCVPR%202024-red)\n* 利用模态特异性特征进行多模态操控检测与定位（Wang 等，2024）\n  [[论文]](https:\u002F\u002Fieeexplore.ieee.org\u002Fstamp\u002Fstamp.jsp?tp=&arnumber=10448385&tag=1)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FICASSP%202024-green)\n\n  \n\n### 理由生成\n* TELLER：一种可解释、可泛化且可控的可信假新闻检测框架（Liu 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.07776)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fless-and-less-bugs\u002FTrust_TELLER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* 基于竞争智慧防御的大语言模型可解释假新闻检测（Wang 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2405.03371)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fwangbo9719\u002FL-Defense_EFND)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* 事实核查解释生成的基准测试（Russo 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2308.15202)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FLanD-FBK\u002Fbenchmark-gen-explanations)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FTACL%202023-orange)\n* “为什么这是误导性的？”：通过解释检测新闻标题幻觉（Shen 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.01060.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* 基于显著性感知图学习探索多跳事实核查的忠实理由（Si 等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.01060.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FAAAI%202023-purple)\n* 面向公共卫生声明的可解释自动化事实核查（Kotonya 和 Toni，2020）。\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.09926)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fneemakot\u002FHealth-Fact-Checking)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fneemakot\u002FHealth-Fact-Checking)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202020-orange)\n* 事实核查解释的生成（Atanasova 等，2020）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.acl-main.656.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202020-orange)\n* GCAN：面向社交媒体上可解释假新闻检测的图感知协同注意力网络（Lu 和 Li，2020）。\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.acl-main.48.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fl852888\u002FGCAN)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202020-orange)\n* DTCA：基于决策树的协同注意力网络用于可解释主张验证（Wu 等，2020）。\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.acl-main.97.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202020-orange)\n* ExFaKT：一个用于在知识图谱和文本上解释事实的框架（Gad-Elrab 等，2019）\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3289600.3290996)\n  [[代码]](https:\u002F\u002Fwww.mpi-inf.mpg.de\u002Fimpact\u002Fexfakt)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWSDM%202019-blue)\n* dEFEND：可解释假新闻检测（Shu 等，2019）。\n  [[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002Fpdf\u002F10.1145\u002F3292500.3330935)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FKDD%202019-blue)\n* 基于概率答案集编程的可解释事实核查\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.09198)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fppapotti\u002Fexpclaim)\n* 你的证据在哪里：通过理由建模改进事实核查（Alhindi 等，2018）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FW18-5513.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FTariq60\u002FLIAR-PLUS)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFEVER@EMNLP%202018-orange)\n* DeClarE：利用证据感知深度学习揭穿假新闻和虚假主张（Popat 等，2018）。\n  [[论文]](https:\u002F\u002Faclanthology.org\u002FD18-1003.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202019-orange)\n\n\n\n\n## 相关任务\n\n### 大语言模型中的事实性\n* 大型语言模型中的长篇幅事实性（Wei 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.18802)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fgoogle-deepmind\u002Flong-form-factuality)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNeurIPS%202024-purple)\n* 多语言 FActScore 的分析（Kim 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2406.19276)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* VeriScore：评估长篇文本生成中可验证主张的事实性（Song 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2406.19276)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FYixiao-Song\u002FVeriScore)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202024-orange)\n* FActScore：对长篇文本生成中事实精确度的细粒度原子级评估（Min 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.14251)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fshmsw25\u002FFActScore)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202023-orange)\n* FacTool：生成式 AI 中的事实性检测——一个用于多任务和多领域场景的工具增强框架（Chern 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2307.13528)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FGAIR-NLP\u002Ffactool)\n\n### 检测大语言模型生成文本\n* MiRAGeNews：多模态真实AI生成新闻检测（Huang等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2410.09045)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fnosna\u002Fmiragenews)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202024-orange)\n* 仅十个词仍有助益：通过代理引导的高效重采样改进黑盒AI生成文本检测（Shi等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.09199)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FICTMCG\u002FPOGER)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202024-purple)\n* 检测人机协作混合文本中的AI生成句子：挑战、策略与洞察（Zeng等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.03506)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fdouglashiwo\u002FAISentenceDetection)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202024-purple)\n* GPT生成文本检测：基准数据集与基于张量的检测方法（Qazi等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.07321)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* 检测对话式搜索中的生成原生广告（Schmidt等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.04889)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fwebis-de\u002FWWW-24)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* BUST：用于评估LLM生成文本检测器的基准测试（Cornelius等，2024）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2024.naacl-long.444.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002FIDSIA-NLP\u002FBUST)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* GPT-who：基于信息密度的机器生成文本检测器（Venkatraman等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2310.06202)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fsaranya-venkatraman\u002Fgpt-who)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* LLM作为合著者：能否检测到人类撰写与机器生成混合文本？（Zhang等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2401.05952)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FDongping-Chen\u002FMixSet)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n\n\n### 虚假信息与错误信息\n* 带有法律后果的虚假信息（MisLC）：一项旨在利用虚假信息社会危害性的新任务（Luo等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2410.03829)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fchufeiluo\u002Fmislc)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202024-orange)\n* 解码易感性：通过计算方法建模对虚假信息的错误信念（Liu等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2311.09630)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@EMNLP%202024-orange)\n* 整合论证与仇恨言论技术以对抗虚假信息（Saha等，2024）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2024.emnlp-main.622.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fsougata-ub\u002FMisInfoCorrected)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202024-orange)\n* 模拟虚假信息易感性（SMISTS）：利用大型语言模型模拟增强虚假信息研究（Ma等，2024）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2024.findings-acl.162\u002F)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* DELL：为基于LLM的虚假信息检测生成反应与解释（Wan等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.10426)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fwhr000001\u002FDELL)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* （不）取消关注虚假信息传播者的动态（Ashkinaze等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2401.13480)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202024-blue)\n* 面向在线虚假信息的证据驱动型检索增强响应生成（Yue等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2403.14952)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNAACL%202024-orange)\n* 通过细粒度情感分析预微调提升破坏社会稳定的虚假信息检测（Pan等，2024）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2024.findings-eacl.92.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEACL%202024-orange)\n* 人机协作评估用于早期虚假信息检测：以COVID-19治疗为例\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2212.09683.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fethanm88\u002Fhitl-evaluation-early-misinformation-detection)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202023-orange)\n* 基于强化学习的反虚假信息响应生成：以COVID-19疫苗虚假信息为例（He等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2303.06433.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fclaws-lab\u002FMisinfoCorrect)\n  [[代码]](https:\u002F\u002Fgithub.com\u002Fclaws-lab\u002FMisinfoCorrect)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202023-blue)\n* 谁资助虚假信息？对假新闻网站广告相关盈利模式的系统分析（Papadogiannakis等，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2202.05079.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWWW%202023-blue)\n* 虚假信息、错误信息与在线宣传（Guess和Lyons，2020）\n  [[论文]](https:\u002F\u002Fwww.cambridge.org\u002Fcore\u002Fbooks\u002Fsocial-media-and-democracy\u002Fmisinformation-disinformation-and-online-propaganda\u002FD14406A631AA181839ED896916598500\u002Fcore-reader)\n\n\n### 检测先前声明\n* 协助人工事实核查员：检测文档中所有先前已核实的声明（Shaar等，2022）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2109.07410.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FEMNLP%202022-orange)\n* 基于记忆增强的关键句匹配的文章重新排序，用于检测先前已核实的声明（Sheng等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.425.pdf)\n  [[代码]](https:\u002F\u002Fgithub.com\u002FICTMCG\u002FMTM)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* 声明匹配超越英语，以扩展全球事实核查范围（Kazemi等，2021）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2021.acl-long.347.pdf)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202021-orange)\n* CLEF-2021 CheckThat! 实验室：检测值得核查的声明、先前已核实的声明以及假新闻（Nakov等，2021）\n  [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-72240-1_75)\n* 那是众所周知的谎言：检测先前已核实的声明（Shaar等，2020）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.acl-main.332.pdf)\n  [[数据集]](https:\u002F\u002Fgithub.com\u002Fsshaar\u002FThat-is-a-Known-Lie)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FACL%202020-orange)\n* COVIDLies：检测社交媒体上的COVID-19虚假信息（Hossain等，2020）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.nlpcovid19-2.11.pdf)\n* CheckThat! 2020概述：社交媒体中声明的自动识别与验证（Barrón-Cedeño等，2020）\n  [[论文]](https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007%2F978-3-030-58219-7_17)\n\n### 对抗攻击\n* 生成式搜索引擎在对抗性事实核查问题上的鲁棒性评估（Hu 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.12077)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFindings@ACL%202024-orange)\n* 针对基于图的假新闻检测器的通用黑盒对抗攻击（等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2404.15744)\n  ![](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FIJCAI%202024-purple)\n\n\n## 相关综述\n\n### 自动化事实核查\n* 用于事实核查中主张真伪判断的自动化论证生成：架构与方法综述（Eldifrawi 等，2024）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2407.12853)\n* 科学事实核查：资源与方法综述（Vladika 和 Matthes，2023）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2305.16859.pdf)\n* 多模态虚假信息检测综述（Alam 等，2021）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2103.12541.pdf)\n* 自动化事实核查：综述（Zeng 等，2021）\n  [[论文]](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Ffull\u002F10.1111\u002Flnc3.12438)\n* 向可解释的事实核查迈进（Isabelle Augenstein，2021）\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2108.10274.pdf)\n* 可解释的自动化事实核查：综述（Kotonya 和 Toni，2020）\n  [[论文]](https:\u002F\u002Faclanthology.org\u002F2020.coling-main.474.pdf)\n* 用于假新闻检测的自然语言处理综述（Oshikawa 等，2020）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.lrec-1.747.pdf)\n* 事实提取与验证综述：以 FEVER 为例（Bekoulis 等，2020）。\n  [[论文]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2010.03001)\n* 自动化事实核查：任务定义、方法及未来方向（Thorne 和 Vlachos，2018）。\n  [[论文]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002FC18-1283.pdf)\n\n* 从内容管理视角看事实核查（Cazalens 等，2018）。\n  [[论文]](https:\u002F\u002Fhal.archives-ouvertes.fr\u002Fhal-01722666\u002Fdocument)\n\n### 假新闻检测\n* 假新闻综述：基础理论、检测方法及机遇（Zhou 和 Zafarani，2020）。\n[[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3395046)\n* 假新闻与谣言检测技术综述（Bondielli 和 Marcelloni，2020）。\n[[论文]](https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fabs\u002Fpii\u002FS0020025519304372?via%3Dihub)\n* 机器能否学会检测假新闻？聚焦社交媒体的综述（da Silva 等，2019）\n[[论文]](https:\u002F\u002Fscholarspace.manoa.hawaii.edu\u002Fhandle\u002F10125\u002F59713)\n* 基于立场分类的假新闻检测：综述（Lillie 和 Middelboe，2019）。\n[[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1907.00181.pdf)\n* 假新闻的科学（Lazer 等，2018） \n[[论文]](https:\u002F\u002Fscience.sciencemag.org\u002Fcontent\u002F359\u002F6380\u002F1094)\n* 富媒体假新闻检测：综述（Parikh 和 Atrey，2018）。\n[[论文]](https:\u002F\u002Fwww.albany.edu\u002F~sp191221\u002Fpublications\u002FFake_Media_Rich_News_Detection_A_Survey.pdf)\n* 社交媒体上的假新闻检测：数据挖掘视角（Shu 等，2017）。\n[[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1708.01967.pdf)\n\n### 主张检测相关\n* 深度学习在在线社交网络中的虚假信息检测：综述与新视角（Islam 等，2020）\n[[论文]](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007%2Fs13278-020-00696-x)\n* 计算宣传检测综述（Da San Martino 等，2020）。 \n[[论文]](https:\u002F\u002Fwww.ijcai.org\u002Fproceedings\u002F2020\u002F0672.pdf)\n* 社交媒体、政治极化与政治虚假信息：科学文献综述（Tucker 等，2018）\n  [[论文]](https:\u002F\u002Fwww.hewlett.org\u002Fwp-content\u002Fuploads\u002F2018\u002F03\u002FSocial-Media-Political-Polarization-and-Political-Disinformation-Literature-Review.pdf)\n* 社交媒体中谣言的检测与化解：综述（Zubiaga 等，2018）。\n[[论文]](http:\u002F\u002Fkddlab.zjgsu.edu.cn:7200\u002Fresearch\u002Frumor\u002FDetection%20and%20Resolution%20of%20Rumours%20in%20Social%20Media_%20A%20Survey.pdf)\n\n### 立场检测\n* 用于误传和虚假信息识别的立场检测综述（Hardalov 等，2021）\n[[论文]](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2103.00242.pdf)\n* 立场检测：综述（Küçük 和 Can，2020）\n[[论文]](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3369026)\n\n\n## 教程\n* 防范与检测大型语言模型生成的虚假信息 [[Liu 等，SIGIR 2024]](https:\u002F\u002Fsigir24-llm-misinformation.github.io\u002F)\n* 事实核查、假新闻、宣传与媒体偏见：后真相时代的求真之旅 [[Nakov 和 Da San Martino，EMNLP 2020]](https:\u002F\u002Fpropaganda.qcri.org\u002Femnlp20-tutorial\u002F)。\n* 利用 NLP 检测与化解谣言和虚假信息 [[Derczynski 和 Zubiaga，COLING 2020]](https:\u002F\u002Fwww.aclweb.org\u002Fanthology\u002F2020.coling-tutorials.4.pdf) [[幻灯片]](https:\u002F\u002Fdocs.google.com\u002Fpresentation\u002Fd\u002F1ZBVPtHcVgJW2c_ibrdVuoCH7sU9ha8NS7Fq9GCnBnls\u002Fedit?usp=sharing)。\n* 事实核查：理论与实践 [[Dong 等，KDD 2018]](https:\u002F\u002Fshiralkarprashant.github.io\u002Ffact-checking-tutorial-KDD2018\u002F)。\n\n\u003C!-- omit in toc -->\n## ⭐ 星标历史\n\n\u003Ca href=\"https:\u002F\u002Fstar-history.com\u002F#Cartus\u002FAutomated-Fact-Checking-Resources&Date\">\n \u003Cpicture>\n   \u003Csource media=\"(prefers-color-scheme: dark)\" srcset=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FCartus_Automated-Fact-Checking-Resources_readme_b56c89cfedb6.png&theme=dark\" \u002F>\n   \u003Csource media=\"(prefers-color-scheme: light)\" srcset=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FCartus_Automated-Fact-Checking-Resources_readme_b56c89cfedb6.png\" \u002F>\n   \u003Cimg alt=\"星标历史图表\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FCartus_Automated-Fact-Checking-Resources_readme_b56c89cfedb6.png\" \u002F>\n \u003C\u002Fpicture>\n\u003C\u002Fa>","# Automated-Fact-Checking-Resources 快速上手指南\n\n**项目简介**：\n`Automated-Fact-Checking-Resources` 并非一个可直接安装的软件包或 Python 库，而是一个** curated（精选）的资源仓库**。它汇总了自动化事实核查（AFC）领域的学术论文、数据集、共享任务、模型代码链接及相关综述。本指南将指导开发者如何高效利用该仓库获取研究所需的核心资源。\n\n## 1. 环境准备\n\n由于本项目主要是资源索引，无需特定的运行时环境。但为了使用仓库中链接到的具体模型和数据集，建议准备以下基础开发环境：\n\n*   **操作系统**：Linux (推荐), macOS, 或 Windows\n*   **版本控制**：Git\n*   **编程语言**：Python 3.8+ (大多数关联模型依赖)\n*   **深度学习框架**：PyTorch 或 TensorFlow (根据具体引用的模型而定)\n*   **网络环境**：由于部分资源托管在 GitHub、Hugging Face 或学术网站，国内用户建议配置科学上网环境或使用代理加速下载。\n\n## 2. 安装步骤（获取资源）\n\n本项目无需通过 `pip` 或 `conda` 安装。请直接克隆仓库到本地以获取完整的资源列表和论文索引。\n\n```bash\n# 克隆仓库\ngit clone https:\u002F\u002Fgithub.com\u002FCartus\u002FAutomated-Fact-Checking-Literature.git\n\n# 进入目录\ncd Automated-Fact-Checking-Literature\n```\n\n> **提示**：如果直接克隆速度较慢，可使用国内镜像源加速（如 Gitee 镜像，若有）或通过代理设置：\n> ```bash\n> git clone https:\u002F\u002Fghproxy.com\u002Fhttps:\u002F\u002Fgithub.com\u002FCartus\u002FAutomated-Fact-Checking-Literature.git\n> ```\n\n## 3. 基本使用\n\n本仓库的核心价值在于其结构化的分类索引。以下是获取特定资源的标准流程：\n\n### 3.1 浏览任务定义与框架\n打开根目录下的 `README.md` 文件，查看 **Task Definition** 部分。该部分定义了自动化事实核查的三大核心阶段：\n1.  **Claim Detection** (主张检测)\n2.  **Evidence Retrieval** (证据检索)\n3.  **Verdict Prediction** (结论预测)\n\n### 3.2 查找并获取数据集\n根据您的需求，在 `README.md` 的 **Datasets** 章节查找对应子类别。\n\n**示例：获取用于“主张检测”的多模态数据集**\n1.  定位到 `Claim Detection and Extraction Dataset` 小节。\n2.  找到目标项目，例如 `MR2` (Multimodal Retrieval-Augmented Rumor Detection)。\n3.  点击对应的 `[[Dataset]]` 链接（通常指向 GitHub 仓库）。\n4.  执行下载命令（以 MR2 为例）：\n\n```bash\n# 示例：克隆 MR2 数据集仓库\ngit clone https:\u002F\u002Fgithub.com\u002FTHU-BPM\u002FMR2.git\n```\n\n### 3.3 查找模型代码\n在 **Models** 章节，您可以找到复现论文结果的官方代码链接。\n\n**示例：寻找“真实性分类”模型**\n1.  定位到 `Models` -> `Verdict Prediction` -> `Veracity Classification`。\n2.  选择感兴趣的论文条目，点击其代码仓库链接。\n3.  按照该独立仓库中的 `README` 进行具体的模型训练和推理。\n\n### 3.4 追踪最新进展\n关注 `README.md` 顶部的 **Updates** 部分，该项目定期更新顶级会议（如 EMNLP, NeurIPS, ACL, WWW）的最新论文。\n*   **2024.12 更新**：增加了大语言模型（LLM）事实性章节及 EMNLP\u002FNeurIPS 2024 论文。\n*   **2024.06 更新**：增加了 LLM 生成文本检测及相关任务。\n\n---\n**注意**：本仓库本身不包含可执行代码。所有具体的安装、训练和评估步骤，请参考您从本仓库链接跳转到的各个子项目（论文官方代码库）的说明文档。","某新闻科技公司的算法团队正致力于研发新一代多模态假新闻检测系统，需要快速构建从“观点提取”到“证据检索”再到“真伪判定”的全流程模型。\n\n### 没有 Automated-Fact-Checking-Resources 时\n- **文献搜集效率低下**：研究人员需手动在 ACL、EMNLP 等各大会议中筛选论文，耗时数周仍难以覆盖最新的 LLM 幻觉检测或多模态核查成果。\n- **数据标准不统一**：面对分散的自然观点与人工构造观点数据集，团队难以界定任务边界，导致训练数据清洗和标注规范反复返工。\n- **技术选型盲目**：缺乏对现有模型（如观点检测、理由生成）的系统性对比，容易重复造轮子或选用已过时的基线模型。\n- **框架认知模糊**：团队成员对自动化事实核查的三阶段流程理解不一，尤其在处理图文混合证据时，难以形成统一的技术架构。\n\n### 使用 Automated-Fact-Checking-Resources 后\n- **前沿资源一键获取**：直接利用仓库中更新的 2024 年 NeurIPS、WWW 等顶会论文列表，迅速掌握大模型事实性及多模态核查的最新进展。\n- **数据集分类清晰**：依托仓库对“观点检测”、“真伪分类”及“脱离语境分类”等数据集的细致划分，快速锁定适配业务场景的高质量数据。\n- **模型路线明确**：参考仓库整理的各类任务 SOTA 模型与共享任务成果，直接复用成熟的基线代码，将研发启动时间缩短 60%。\n- **架构设计标准化**：基于仓库提供的统一 NLP 框架图示，团队迅速对齐了从多模态观点提取到理由生成的全流程定义，减少了沟通成本。\n\nAutomated-Fact-Checking-Resources 通过提供结构化、实时更新的全景式资源地图，将原本碎片化的研究路径转化为高效的工程落地指南。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FCartus_Automated-Fact-Checking-Resources_55ad1902.png","Cartus","Zhijiang","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FCartus_e747e8c9.jpg","NLP\u002FML\u002FLLM Padawan","University of Cambridge","Tonga",null,"ZhijiangG","https:\u002F\u002Fcartus.github.io\u002F","https:\u002F\u002Fgithub.com\u002FCartus",562,62,"2026-04-13T01:03:16","MIT",5,"","未说明",{"notes":90,"python":88,"dependencies":91},"该仓库是一个资源列表（文献、数据集、任务、模型链接的集合），而非一个可直接运行的软件工具或代码库。README 中未包含任何关于安装、环境配置、依赖库或硬件需求的技术说明。用户需根据列表中链接到的具体论文或子项目（如特定的 GitHub 模型仓库）去查询各自的运行环境需求。",[],[16,35,93,14],"其他",[95,96,97,98,99,100,101,102,103,104,105,106],"natural-language-processing","machine-learning","survey","fact-checking","fake-news-detection","rumor-detection","paper-list","information-retrieval","claim-detection","natural-language-inference","explainable-ai","dataset","2026-03-27T02:49:30.150509","2026-04-18T09:19:17.916720",[110,115],{"id":111,"question_zh":112,"answer_zh":113,"source_url":114},39220,"Vlachos & Riedel (2014) 事实核查数据集的原始链接失效了，在哪里可以找到该数据集？","由于 Google Sites 服务停止，原始链接已失效。作者 Andreas Vlachos 提供了 Dropbox 上的可用下载链接：\n1. ODS 格式：https:\u002F\u002Fwww.dropbox.com\u002Fs\u002Fuvwbpjytogqnm68\u002FFactChecking_LTCSS2014_release.ods?dl=0\n2. TSV 格式：https:\u002F\u002Fwww.dropbox.com\u002Fs\u002Fl0nfaezwdpu2w6r\u002FFactChecking_LTCSS2014_release.tsv?dl=0\n也可以访问作者的出版物页面获取更多资源：https:\u002F\u002Fandreasvlachos.github.io\u002F\u002Fpublications\u002F","https:\u002F\u002Fgithub.com\u002FCartus\u002FAutomated-Fact-Checking-Resources\u002Fissues\u002F7",{"id":116,"question_zh":117,"answer_zh":118,"source_url":119},39221,"HOVER 数据集的标签（verdict）应该定义为几类？","HOVER 数据集的 verdict 应当被定义为目标两类（two classes）。维护者已确认该问题并将相应修改论文中的描述。","https:\u002F\u002Fgithub.com\u002FCartus\u002FAutomated-Fact-Checking-Resources\u002Fissues\u002F3",[]]