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Links to conference/journal publications in automated fact-checking (resources for the TACL22/EMNLP23 paper).

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Automated Fact-Checking Resources

Maintenance Last Commit Contribution_welcome

Updates:

  • 2024.8: Added papers from WWW, IJCAI, and ACL 2024
  • 2024.6: Added a section for LLM-generated text in Related Tasks. Added papers from EACL, NAACL, AAAI, ICLR 2024

Overview

This repo contains relevant resources from our survey paper A Survey on Automated Fact-Checking in TACL 2022 and the follow up multimodal survey paper Multimodal Automated Fact-Checking: A Survey 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.

Task Definition

Figure below shows a NLP framework for automated fact-checking (AFC) with text consisting of three stages:

  1. Claim detection to identify claims that require verification;
  2. Evidence retrievalto find sources supporting or refuting the claim;
  3. Claim verification to assess the veracity of the claim based on the retrieved evidence.

Framework

Evidence 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.

In 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:

Framework

  1. 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.
  2. Evidence Retrieval: similarly to fact-checking with text, multimodal fact-checking relies on evidence to make judgments.
  3. Verdict Prediction and Justification Production: it is decomposed into three tasks considering prevalent ways that multimodal misinformation can be conveyed:
    • Manipulation Classification: classify misinformative claims with manipulated content or correct claims accompanied by manipulated content.
    • Out-of-context Classification: detect unchanged content from a different context.
    • Veracity Classification: classify the veracity of textual claims given retrieved evidence.

Datasets

Claim Detection and Extraction Dataset

  • MR2: A Benchmark for Multimodal Retrieval-Augmented Rumor Detection in Social Media (Hu et al., 2023) [Paper] [Dataset]
  • FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms (Qi et al., 2023) [Paper] [Dataset]
  • SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse (Hafid et al., 2022) [Paper] [Dataset]
  • Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter (Sundriyal et al., 2022) [Paper] [Dataset]
  • Stanceosaurus: Classifying Stance Towards Multilingual Misinformation (Zheng et al., 2022) [Paper] [Dataset]
  • Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media (Park et al., 2022) [Paper]
  • CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets (Mohr et al., 2022) [Paper] [Dataset]
  • MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset (Nielsen et al., 2022) [Paper] [Dataset]
  • STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media (Rao et al., 2021) [Paper] [Dataset]
  • Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society (Alam et al., 2021) [Paper] [Dataset]
  • Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection (Konstantinovskiy et al., 2021) [Paper]
  • The CLEF-2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News (Nakov et al., 2021) [Paper] [Dataset]
  • Mining Dual Emotion for Fake News Detection (Zhang et al., 2021) [Paper] [Dataset]
  • Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media (Barrón-Cedeño et al., 2020) [Paper] [Dataset]
  • Citation Needed: A Taxonomy and Algorithmic Assessment of Wikipedia's Verifiability (Redi et al., 2019) [Paper] [Dataset]
  • SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours (Gorrell et al., 2019). [Paper] [Dataset]
  • Joint Rumour Stance and Veracity (Lillie et al., 2019) [Paper] [Dataset]
  • Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 1: Check-Worthiness (Atanasova et al., 2018) [Paper] [Dataset]
  • Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter (Volkova et al., 2017) [Paper] [Dataset]
  • A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates (Gencheva et al., 2017) [Paper] [Dataset]
  • Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs (Jin et al., 2017) [Paper]
  • SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours (Derczynski et al., 2017). [Paper] [Dataset]
  • Detecting Rumors from Microblogs with Recurrent Neural Networks (Ma et al., 2016) [Paper] [Dataset]
  • Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads (Zubiaga et al., 2016). [Paper] [Dataset]
  • CREDBANK: A Large-Scale Social Media Corpus with Associated Credibility Annotations (Mitra and Gilbert, 2015). [Paper] [Dataset]
  • Detecting Check-worthy Factual Claims in Presidential Debates (Hassan et al., 2015) [Paper]

Verdict Prediction Dataset

Veracity Classification Dataset

Natural Claims
  • Do Large Language Models Know about Facts? (Xu et al., 2024) [Paper] [Dataset] [Code]
  • ESCNet: Entity-enhanced and Stance Checking Network for Multi-modal Fact-Checking (Zhang et al., 2024) [Paper] [Code]
  • MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection (Li et al., 2024) [Paper] [Dataset]
  • What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification (Wührl et al., 2024) [Paper] [Dataset]
  • AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web (Schlichtkrull et al., 2023) [Paper] [Dataset] [Shared Task]
  • COVID-VTS: Fact Extraction and Verification on Short Video Platforms (Liu et al., 2023) [Paper] [Dataset] [Code]
  • End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models (Yao et al., 2023) [Paper] [Dataset]
  • Modeling Information Change in Science Communication with Semantically Matched Paraphrases (Wright et al., 2022) [Paper] [Dataset] [Code]
  • Generating Literal and Implied Subquestions to Fact-check Complex Claims (Chen et al., 2022) [Paper] [Dataset]
  • SciFact-Open: Towards open-domain scientific claim verification (Wadden et al., 2022) [Paper] [Dataset]
  • CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking (Hu et al., 2022) [Paper] [Dataset]
  • WatClaimCheck: A new Dataset for Claim Entailment and Inference (Khan et al., 2022) [Paper] [Dataset]
  • Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources (Abdelnabi et al., 2022) [Paper] [Dataset]
  • MMM: An Emotion and Novelty-aware Approach for Multilingual Multimodal Misinformation Detection (Gupta et al., 2022) [Paper] [Dataset]
  • FactDrill: A Data Repository of Fact-Checked Social Media Content to Study Fake News Incidents in India (Singhal et al., 2022) [Paper]
  • Evidence-based Fact-Checking of Health-related Claims (Sarrouti et al., 2021) [Paper] [Dataset]
  • COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic (Saakyan et al., 2021) [Paper] [Dataset]
  • Edited Media Understanding Frames: Reasoning About the Intents and Implications of Visual Disinformation (Da et al., 2021) [Paper] [Code]
  • Structurizing Misinformation Stories via Rationalizing Fact-Checks (Jiang et al., 2021) [Paper] [Dataset]
  • X-FACT: A New Benchmark Dataset for Multilingual Fact Checking (Gupta and Srikumar, 2021) [Paper] [Dataset]
  • LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification (Azevedo et al., 2021) [Paper] [Code]
  • Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection (Li et al., 2021) [Paper]
  • Explainable Automated Fact-Checking for Public Health Claims (Kotonya and Toni, 2020b) [Paper] [Dataset]
  • Fact or Fiction: Verifying Scientific Claims (Wadden et al., 2020). [Paper] [Dataset]
  • AnswerFact: Fact Checking in Product Question Answering (Zhang et al., 2020) [Paper] [Dataset]
  • Explainable Automated Fact-Checking for Public Health Claims (Kotonya and Toni, 2020). [Paper] [Dataset]
  • r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection (Nakamura et al., 2020). [Paper] [Dataset]
  • CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims (Diggelmann et al., 2020) [Paper] [Dataset]
  • FakeCovid-- A Multilingual Cross-domain Fact Check News Dataset for COVID-19 (Shahi and Nandini, 2020). [Paper] [Dataset]
  • FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media (Shu et al., 2020). [Paper] [Dataset]
  • A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking (Hanselowski et al., 2019). [Paper] [Code] [Dataset]
  • MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims (Augenstein et al., 2019). [Paper] [Dataset]
  • Fact-Checking Meets Fauxtography: Verifying Claims About Images (Zlatkova et al., 2019) [Paper] [Dataset]
  • FA-KES: A Fake News Dataset around the Syrian War (Salem et al., 2019) [Paper] [Dataset]
  • Fact Checking in Community Forums (Mihaylova et al., 2018) [Paper] [Dataset]
  • EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection [Paper] [Dataset]
  • Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 2: Factuality (Barrón-Cedeño et al., 2018) [Paper] [Dataset]
  • Integrating Stance Detection and Fact Checking in a Unified Corpus (Baly et al., 2018). [Paper] [Dataset]
  • A Stylometric Inquiry into Hyperpartisan and Fake News (Potthast et al., 2018) [Paper] [Dataset]
  • A News Veracity Dataset with Facebook User Commentary and Egos (Santia and Williams, 2018) [Paper]] [Dataset]
  • Sampling the News Producers: A Large News and Feature Data Set for the Study of the Complex Media Landscape (Horne et al., 2018) [Paper] [Dataset]
  • Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking (Rashkin et al., 2017). [Paper] [Dataset]
  • “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection (Wang, 2017). [Paper] [Dataset]
  • Credibility Assessment of Textual Claims on the Web (Popat et al., 2016) [Paper] [Dataset]
  • Emergent: a novel data-set for stance classification (Ferreira and Vlachos, 2016) [Paper] [Dataset]
  • Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News (Rubin et al., 2016) [Paper]
  • Identification and Verification of Simple Claims about Statistical Properties (Vlachos and Riedel, 2015) [Paper] [Dataset]
  • Fact Checking: Task definition and dataset construction (Vlachos and Riedel, 2014) [Paper] [Dataset]
  • Verification and Implementation of Language-Based Deception Indicators in Civil and Criminal Narratives (Bachenko et al., 2008) [Paper]
Artificial Claims
  • EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification (Ma et al., 2024) [Paper] [Code]
  • CFEVER: A Chinese Fact Extraction and VERification Dataset (Lin et al., 2024) [Paper] [Dataset]
  • FACTKG: Fact Verification via Reasoning on Knowledge Graphs (Kim et al., 2023) [Paper] [Code] [Dataset]
  • Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation (Huang et al., 2023) [Paper] [Code] [Dataset]
  • FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering (Rani et al., 2023) [Paper]
  • Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking (Akhtar et al., 2023) [Paper] [Dataset]
  • Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines (Gabriel et al., 2022) [Paper] [Dataset]
  • DialFact: A Benchmark for Fact-Checking in Dialogue (Gupta et al., 2022) [Paper] [Dataset]
  • FAVIQ: FAct Verification from Information-seeking Questions (Park et al., 2022) [Paper] [Dataset]
  • FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information (Aly et al., 2021)
    [Paper] [Dataset] [Code]
  • Statement Verification and Evidence Finding with Tables (SEM-TAB-FACT) (Wang et al., 2021) [Dataset]
  • Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (Schuster et al., 2021) [Paper] [Dataset]
  • ParsFEVER: a Dataset for Farsi Fact Extraction and Verification (Zarharan et al., 2021) [Paper] [Dataset]
  • DanFEVER: claim verification dataset for Danish (Nørregaard and Derczynski, 2021) [Paper] [Dataset]]
  • HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification (Jiang et al., 2020) [Paper] [Dataset]
  • INFOTABS: Inference on Tables as Semi-structured Data (Gupta et al., 2020) [Paper] [Dataset]
  • TabFact: A Large-scale Dataset for Table-based Fact Verification (Chen et al., 2020) [Paper] [Dataset]
  • Unsupervised Fact Checking by Counter-Weighted Positive and Negative Evidential Paths in A Knowledge Graph (Kim and Choi, 2020) [Paper]
  • Stance Prediction and Claim Verification: An Arabic Perspective (Khouja, 2020) [Paper] [Dataset]
  • Automated Fact-Checking of Claims from Wikipedia (Sathe et al., 2020). [Paper] [Dataset]
  • FEVER: a Large-scale Dataset for Fact Extraction and VERification (Thorne et al., 2018). [Paper] [Dataset]]
  • Automatic Detection of Fake News (Pérez-Rosas et al., 2018) [Paper] [Dataset]]
  • The Lie Detector: Explorations in the Automatic Recognition of Deceptive Language (Mihalcea and Strapparava, 2009) [Paper]
  • Finding Streams in Knowledge Graphs to Support Fact Checking (Shiralkar et al., 2017) [Paper] [Dataset]
  • Discriminative predicate path mining for fact checking in knowledge graphs (Shi and Weninger, 2016) [Paper]
  • Computational fact checking from knowledge networks (Ciampaglia et al., 2015) [Paper]

Manipulation Classification Dataset

  • DF-Platter: Multi-Face Heterogeneous Deepfake Dataset (Narayan et al., 2023) [Paper] [Dataset]
  • Detecting and Grounding Multi-Modal Media Manipulation. (Shao et al., 2023) [Paper] [Dataset]
  • FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset (Khalid et al., 2021) [Paper] [Dataset]
  • Half-Truth: A Partially Fake Audio Detection Dataset (Yi et al., 2021) [Paper]
  • KoDF: A Large-scale Korean DeepFake Detection Dataset (Kwon et al., 2021) [Paper] [Dataset]
  • Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics (Li et al., 2020) [Paper] [Dataset]
  • DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection (Jiang et al., 2020) [Paper] [Dataset]
  • DeepSonar: Towards Effective and Robust Detection of AI-Synthesized Fake Voices (Wang et al., 2020) [Paper]
  • FoR: A Dataset for Synthetic Speech Detection (Reimao et al., 2019) [Paper]
  • Phonespoof: A New Dataset for Spoofing Attack Detection in Telephone Channel (Lavrentyeva et al., 2019) [Paper]
  • The Deepfake Detection Challenge (DFDC) Preview Dataset (Dolhansky et al., 2019) [Paper] [Dataset]
  • The PS-Battles Dataset -- an Image Collection for Image Manipulation Detection (Heller et al., 2018) [Paper] [Dataset]
  • FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces (Rossler et al., 2018) [Paper] [Dataset]

Out-of-Context Classification Dataset

  • Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines (Sung et al., 2023) [Paper] [Dataset]
  • COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning (Aneja et al., 2023) [Paper] [Code] [Dataset]
  • Factify 2: A multimodal fake news and satire news dataset (Suryavardan et al., 2023) [Paper] [Dataset]
  • InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection (Fung et al., 2021) [Paper] [Dataset]
  • NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media (Luo et al., 2021) [Paper] [Dataset]
  • Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News (Tan et al., 2020) [Paper] [Dataset]
  • Multimodal analytics for real-world news using measures of cross-modal entity consistency (Müller-Budack et al., 2020) [Paper] [Dataset]
  • Deep Multimodal Image-Repurposing Detection (Sabir et al., 2018) [Paper] [Dataset]
  • Multimedia semantic integrity assessment using joint embedding of images and text (Jaiswal et al., 2017) [Paper]

Shared Tasks

Models

Claim Detection and Extraction

  • Document-level Claim Extraction and Decontextualisation for Fact-Checking (Deng et al., 2024) [Paper]
  • Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models (Yang et al., 2024) [Paper] [Code]
  • Unveiling Opinion Evolution via Prompting and Diffusion for Short Video Fake News Detection(Zong et al., 2024) [Paper]
  • From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News (Liu et al., 2024) [Paper]
  • Heterogeneous Subgraph Transformer for Fake News Detection (Zhang et al., 2024) [Paper]
  • Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection (Tao et al., 2024) [Paper]
  • T3RD: Test-Time Training for Rumor Detection on Social Media (Zhang et al., 2024) [Paper] [Code]
  • Dual Graph Networks with Synthetic Oversampling for Imbalanced Rumor Detection on Social Media (Lu et al., 2024) [Paper]
  • Rumor Mitigation in Social Networks with Deep Reinforcement Learning (Su et al., 2024) [Paper]
  • Adapting Fake News Detection to the Era of Large Language Models (Su et al., 2024) [Paper] [Code]
  • An Interactive Framework for Profiling News Media Sources (Mehta et al., 2024) [Paper] [Code]
  • CMA-R:Causal Mediation Analysis for Explaining Rumour Detection (Tian et al., 2024) [Paper] [Code]
  • Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection (Wang et al., 2024) [Paper]
  • Reinforced Adaptive Knowledge Learning for Multimodal Fake News Detection (Zhang et al., 2024) [Paper]
  • Unveiling Implicit Deceptive Patterns in Multi-Modal Fake News via Neuro-Symbolic Reasoning (Dong et al., 2024) [Paper] [Code]
  • Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning Approach for Rumor Detection (Cui et al., 2024) [Paper]
  • Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector (Lao et al., 2024) [Paper] [Code]
  • GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking (Yin et al., 2024) [Paper]
  • Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning (Xu et al., 2024) [Paper] [Code]
  • Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection (Hu et al., 2024) [Paper] [Code]
  • Interpretable Multimodal Misinformation Detection with Logic Reasoning (Liu et al., 2023) [Paper] [Code]
  • Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors (Qi et al., 2023) [Paper] [Code]
  • Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection (Hu et al., 2023) [Paper] [Code]
  • Causal Intervention and Counterfactual Reasoning for Multi-modal Fake News Detection (Chen et al., 2023) [Paper]
  • MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning (Yue et al., 2023) [Paper] [Code]
  • Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning (Lin et al., 2023) [Paper]
  • Unsupervised Cross-Domain Rumor Detection with Contrastive Learning and Cross-Attention (Ran et al., 2023) [Paper]
  • Zoom Out and Observe: News Environment Perception for Fake News Detection (Sheng et al., 2022) [Paper] [Code]
  • DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media (Sun et al., 2022) [Paper]
  • Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks (Lin et al., 2021) [Paper]
  • STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media (Rao et al., 2021) [Paper] [Code]
  • Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection (Sun et al., 2021) [Paper] [Code]
  • Active Learning for Rumor Identification on Social Media (Farinneya et al., 2021) [Paper]
  • Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection (Wei et al., 2021) [Paper] [Code]
  • Adversary-Aware Rumor Detection (Song et al., 2021) [Paper] [Code]
  • Learning Disentangled Latent Topics for Twitter Rumour Veracity Classification (Dougrez-Lewis et al., 2021) [Paper] [Code]
  • Mining Dual Emotion for Fake News Detection (Zhang et al., 2021). [Paper] [Code]
  • Claim Check-Worthiness Detection as Positive Unlabelled Learning (Wright and Augenstein, 2021) [Paper] [Code]
  • Exploiting Microblog Conversation Structures to Detect Rumors (Li et al., 2020). [Paper]
  • Debunking Rumors on Twitter with Tree Transformer (Ma et al., 2020) [Paper]
  • VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text (Cheng et al., 2020) [Paper] [Code]
  • Rumor Detection on Social Media with Graph Structured Adversarial Learning (Yang et al., 2020) [Paper]
  • Interpretable Rumor Detection in Microblogs by Attending to User Interactions (Khoo et al., 2020) [Paper] [Code]
  • Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks (Bian et al., 2020) [Paper] [Code]
  • Fake News Early Detection: A Theory-driven Model (Zhou et al., 2020). [Paper]
  • MVAE: Multimodal Variational Autoencoder for Fake News Detection (Khattar et al., 2019). [Paper] [Code]
  • Fake News Detection on Social Media using Geometric Deep Learning (Monti et al., 2019). [Paper]
  • Rumor Detection on Twitter with Tree-structured Recursive Neural Networks (Ma et al., 2018). [Paper] [Code]
  • Rumor Detection with Hierarchical Social Attention Network (Guo et al., 2018). [Paper]
  • A Hybrid Recognition System for Check-worthy Claims Using Heuristics and Supervised Learning (Zuo et al., 2018). [Paper]
  • Simple Open Stance Classification for Rumour Analysis (Aker et al., 2017). [Paper]
  • NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter (Enayet and El-Beltagy, 2017). [Paper]
  • Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM (Kochkina et al., 2017). [Paper]
  • Automatically Identifying Fake News in Popular Twitter Threads (Buntain and Golbeck, 2017). [Paper]
  • Detecting Rumors from Microblogs with Recurrent Neural Networks (Ma et al., 2016). [Paper] [Dataset]

Verdict Prediction

Veractiy Classification

  • ChartCheck: Explainable Fact-Checking over Real-World Chart Images (Akhtar et al., 2024) [Paper] [Code]
  • Evidence Retrieval is almost All You Need for Fact Verification (Zheng et al., 2024) [Paper]
  • Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments (Yue et al., 2024) [[Paper]]https://arxiv.org/pdf/2406.09815() [Code]
  • MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking(et al., 2024) [Paper]
  • VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning (Niu et al., 2024) [Paper] [Demo]
  • Event-Radar: Event-driven Multi-View Learning for Multimodal Fake News Detection(et al., 2024) [Paper]
  • Unified Evidence Enhancement Inference Framework for Fake News Detection (Wu et al., 2024) [Paper]
  • Natural Language-centered Inference Network for Multi-modal Fake News Detection (Zhang et al., 2024) [Paper]
  • From Creation to Clarification: ChatGPT's Journey Through the Fake News Quagmire (Huang et al., 2024) [Paper]
  • MSynFD: Multi-hop Syntax aware Fake News Detection (Liang et al., 2024) [Paper]
  • Fighting against Fake News on Newly-Emerging Crisis: A Case Study of COVID-19 (Yang et al., 2024) [Paper] [Code]
  • Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models () [Paper]
  • Fact Checking Beyond Training Set (Karisani et al., 2024) [Paper] [Code]
  • Language Models Hallucinate, but May Excel at Fact Verification (Guan et al., 2024) [Paper] [Code]
  • Complex Claim Verification with Evidence Retrieved in the Wild (Chen et al., 2024) [Paper] [Code]
  • MAPLE: Micro Analysis of Pairwise Language Evolution for Few-Shot Claim Verification (Zeng et al., 2024) [Paper] [Code]
  • Rethinking Loss Functions for Fact Verification (Mukobara et al., 2024) [Paper] [Code]
  • Comparing Knowledge Sources for Open-Domain Scientific Claim Verification (Vladika et al., 2024) [Paper] [Code]
  • Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment (Zhang et al., 2024) [Paper] [Code]
  • Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables (Gong et al., 2024) [Paper] [Code]
  • DECKER: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification (Zou et al., 2023) [Paper] [Code]
  • Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence (Wang et al., 2023) [Paper] [Code]
  • Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction (Fajcik et al., 2023) [Paper] [Code]
  • 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) [Paper] [Code]
  • Counterfactual Debiasing for Fact Verification (Xu et al., 2023) [Paper]
  • Fact-Checking Complex Claims with Program-Guided Reasoning (Pan et al., 2023) [Paper] [Code]
  • Bootstrapping Multi-view Representations for Fake News Detection (Ying et al., 2023) [Paper]
  • Varifocal Question Generation for Fact-checking (Ousidhoum et al., 2022) [Paper]
  • ProoFVer: Natural Logic Theorem Proving for Fact Verification (Krishna et al., 2022) [Paper]
  • MultiVerS: Improving scientific claim verification with weak supervision and full-document context (Wadden et al., 2022) [Paper] [Code]
  • Generating Scientific Claims for Zero-Shot Scientific Fact Checking (Wright et al., 2022) [Paper] [Code]
  • Automatic Detection of Entity-Manipulated Text Using Factual Knowledge (Jawahar et al., 2022) [Paper] [Code]
  • LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification (Chen et al., 2022) [Paper] [Code]
  • Towards Fine-Grained Reasoning for Fake News Detection (Jin et al., 2022) [Paper]
  • Synthetic Disinformation Attacks on Automated Fact Verification Systems (Du et al., 2021) [Paper] [Code]
  • Editing Factual Knowledge in Language Models (De Cao et al., 2021) [Paper] [Code]
  • Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification (Shi et al., 2021) [Paper] [Code]
  • Students Who Study Together Learn Better: On the Importance of Collective Knowledge Distillation for Domain Transfer in Fact Verification (Mithun et al., 2021) [Paper]
  • Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification (Zhang et al., 2021) [Paper] [Code]
  • Table-based Fact Verification with Salience-aware Learning (Wang et al., 2021) [Paper] [Code]
  • Exploring Decomposition for Table-based Fact Verification (Yang et al., 2021) [Paper] [Code]
  • Joint Verification and Reranking for Open Fact Checking Over Tables (Schlichtkrull et al., 2021). [Paper] [Code]
  • Multi-Task Retrieval for Knowledge-Intensive Tasks (Maillard et al., 2021). [Paper]
  • Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification (Si et al., 2021). [Paper] [Code]
  • A DQN-based Approach to Finding Precise Evidences for Fact Verification (Wan et al., 2021) [Paper] [Code]
  • Unified Dual-view Cognitive Model for Interpretable Claim Verification (Wu et al., 2021) [Paper]
  • Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge (Hu et al., 2021) [Paper] [Code]
  • Automatic Fake News Detection: Are Models Learning to Reason? (Hansen et al., 2021) [Paper] [Code]
  • Exploring Listwise Evidence Reasoning with T5 for Fact Verification (Jiang et al., 2021) [Paper]
  • Multimodal Fusion with Co-Attention Networks for Fake News Detection (Wu et al., 2021) [Paper]
  • A Multi-Level Attention Model for Evidence-Based Fact Checking (Kruengkrai et al., 2021) [Paper] [Code]
  • Strong and Light Baseline Models for Fact-Checking Joint Inference (Tymoshenko et al., 2021) [Paper] [Code]
  • Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020). [Paper] [Code]
  • Language Models as Fact Checkers? (Lee et al., 2020). [Paper]
  • Hierarchical Evidence Set Modeling for Automated Fact Extraction and Verification (Subramanian et al., 2020) [Paper] [Code]
  • Program Enhanced Fact Verification with Verbalization and Graph Attention Network (Yang et al., 2020). [Paper] [Code]
  • Understanding tables with intermediate pre-training (Eisenschlos et al., 2020). [Paper] [Code]
  • Fine-grained Fact Verification with Kernel Graph Attention Network (Liu et al., 2020). [Paper] [Code]
  • Reasoning Over Semantic-Level Graph for Fact Checking (Zhong et al., 2020). [Paper]
  • LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network (Zhong et al., 2020). [Paper]
  • Scrutinizer: A Mixed-Initiative Approach to Large-Scale, Data-Driven Claim Verification (Karagiannis et al., 2020) [Paper] [Code]
  • Unsupervised Question Answering for Fact-Checking (Jobanputra, 2019). [Paper] [Code]
  • GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification (Zhou et al., 2019). [Paper] [Code]]
  • Combining Fact Extraction and Verification with Neural Semantic Matching Networks (Nie et al., 2019). [Paper] [Code]
  • Team DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task (Stammbach and Neumann, 2019). [Paper] [Code]
  • Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks (Ma et al., 2019). [Paper]
  • BERT for Evidence Retrieval and Claim Verification (Soleimani et al., 2019) [Paper] [Code]
  • TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim Verification (Yin and Roth, 2018). [Paper] [Code]
  • UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification (Hanselowski et al., 2018). [Paper] [Code]
  • Team Papelo: Transformer Networks at FEVER (Malon, 2018). [Paper] [Code]
  • QED: A fact verification system for the FEVER shared task (Luken et al., 2018). [Paper] [Code]
  • UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF) (Yoneda et al., 2018). [Paper] [Code]
  • Can Rumour Stance Alone Predict Veracity? (Dungs et al., 2018). [Paper]
  • Varying Shades: Analyzing Language in Fake News and Political Fact-Checking (Rashkin et al., 2017). [Paper]

Manipulation Classification

Out-of-Context Classification

Justification Production

  • TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection (Liu et al., 2024) [Paper] [Code]
  • Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom (Wang et al., 2024) [Paper] [Code]
  • “Why is this misleading?”: Detecting News Headline Hallucinations with Explanations (Shen et al., 2023) [Paper]]
  • Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning (Si et al., 2023) [Paper]]
  • Explainable Automated Fact-Checking for Public Health Claims (Kotonya and Toni, 2020). [Paper]] [Code] [Dataset]
  • Generating Fact Checking Explanations (Atanasova et al., 2020). [Paper]
  • GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (Lu and Li, 2020). [Paper] [Code]
  • DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (Wu et al., 2020). [Paper]
  • ExFaKT: A Framework for Explaining Facts over Knowledge Graphs and Text (Gad-Elrab et al., 2019) [Paper] [Code]
  • dEFEND: Explainable Fake News Detection (Shu et al., 2019). [Paper]
  • Explainable Fact Checking with Probabilistic Answer Set Programming [Paper] [Code]
  • Where is your Evidence: Improving Fact-checking by Justification Modeling (Alhindi et al., 2018). [Paper] [Code]]
  • DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning (Popat et al., 2018). [Paper]

Related Tasks

Detecting LLM Generated Text

  • Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling (Shi et al., 2024) [Paper] [Code]
  • Detecting AI-Generated Sentences in Human-AI Collaborative Hybrid Texts: Challenges, Strategies, and Insights (Zeng et al., 2024) [Paper] [Code]
  • GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method (Qazi et al., 2024) [Paper]
  • Detecting Generated Native Ads in Conversational Search (Schmidt et al., 2024) [Paper] [Code]
  • BUST: Benchmark for the evaluation of detectors of LLM-Generated Text (Cornelius et al., 2024) [Paper] [Dataset]
  • GPT-who: An Information Density-based Machine-Generated Text Detector (Venkatraman et al., 2024) [Paper] [Code]
  • LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected? (Zhang et al., 2024) [Paper] [Code]

Misinformation and Disinformation

  • Simulated Misinformation Susceptibility (SMISTS): Enhancing Misinformation Research with Large Language Model Simulations (Ma et al., 2024) [Paper]
  • DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection (Wan et al., 2024) [Paper] [Code]
  • The Dynamics of (Not) Unfollowing Misinformation Spreaders (Ashkinaze et al., 2024) [Paper]
  • Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation (Yue et al., 2024) [Paper]
  • Enhancing Society-Undermining Disinformation Detection through Fine-Grained Sentiment Analysis Pre-Finetuning (Pan et al., 2024) [Paper]
  • Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments [Paper] [Dataset]
  • Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation (He et al., 2023) [Paper] [Dataset] [Code]
  • Who Funds Misinformation? A Systematic Analysis of the Ad-related Profit Routines of Fake News sites (Papadogiannakis et al., 2023) [Paper]
  • Misinformation, Disinformation, and Online Propaganda (Guess and Lyons, 2020) [Paper]

Detecting Previous Claims

  • Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document (Shaar et al., 2022) [Paper]
  • Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims (Sheng et al. 2021) [Paper] [Code]
  • Claim Matching Beyond English to Scale Global Fact-Checking (Kazemiet al. 2021) [Paper]
  • The CLEF-2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News (Nakov et al., 2021) [Paper]]
  • That is a Known Lie: Detecting Previously Fact-Checked Claims (Shaar et al., 2020) [Paper] [Dataset]
  • COVIDLies: Detecting COVID-19 Misinformation on Social Media (Hossain et al., 2020) [Paper]
  • Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media (Barrón-Cedeño et al., 2020) [Paper]

Adversarial Attack

  • Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions (Hu et al., 2024) [Paper]
  • A General Black-box Adversarial Attack on Graph-based Fake News Detectors (et al., 2024) [Paper]

Relevant Surveys

Automated Fact-Checking

  • Automated Justification Production for Claim Veracity in Fact Checking: A Survey on Architectures and Approaches (Eldifrawi et al., 2024) [Paper]

  • Scientific Fact-Checking: A Survey of Resources and Approaches (Vladika and Matthes, 2023) [Paper]

  • A Survey on Multimodal Disinformation Detection (Alam et al., 2021) [Paper]

  • Automated fact-checking: A survey (Zeng et al., 2021) [Paper]

  • Towards Explainable Fact Checking (Isabelle Augenstein, 2021) [Paper]

  • Explainable Automated Fact-Checking: A Survey (Kotonya and Toni, 2020) [Paper]

  • A Survey on Natural Language Processing for Fake News Detection (Oshikawa et al., 2020). [Paper]

  • A Review on Fact Extraction and VERification: The FEVER case (Bekoulis et al., 2020). [paper]

  • Automated Fact Checking: Task Formulations, Methods and Future Directions (Thorne and Vlachos, 2018). [Paper]

  • A Content Management Perspective on Fact-Checking (Cazalens et al., 2018). [paper]

Fake News Detection

  • A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities (Zhou and Zafarani, 2020). [Paper]
  • A Survey on Fake News and Rumour Detection Techniques (Bondielli and Marcelloni, 2020). [paper]
  • Can Machines Learn to Detect Fake News? A Survey Focused on Social Media (da Silva et al. 2019) [Paper]
  • Fake News Detection using Stance Classification: A Survey (Lillie and Middelboe, 2019). [paper]
  • The science of fake news (Lazer et al. 2018) [Paper]
  • Media-Rich Fake News Detection: A Survey (Parikh and Atrey, 2018). [paper]
  • Fake News Detection on Social Media: A Data Mining Perspective (Shu et al., 2017). [Paper]

Claim Detection Related

  • Deep learning for misinformation detection on online social networks: a survey and new perspectives (Islam et al. 2020) [Paper]
  • A Survey on Computational Propaganda Detection (Da San Martino et al. 2020). [Paper]
  • Social Media, Political Polarization, and Political Disinformation: A Review of the Scientific Literature (Tucker et al., 2018) [Paper]
  • Detection and Resolution of Rumours in Social Media: A Survey (Zubiaga et al., 2018). [Paper]

Stance Detection

  • A Survey on Stance Detection for Mis- and Disinformation Identification (Hardalov et al. 2021) [Paper]
  • Stance Detection: A Survey (Küçük and Can 2020) [Paper]

Tutorials