Automated Fact-Checking Resources
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 this survey, we present a comprehensive and up-to-date survey of automated fact-checking (AFC), unifying various components and definitions developed in previous research into a common framework. As automated fact-checking research is evolving, we will provided timely update 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:
- Claim detection to identify claims that require verification;
- Evidence retrievalto find sources supporting or refuting the claim;
- Claim verification to assess the veracity of the claim based on the retrieved evidence.
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:
- 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.
- Evidence Retrieval: similarly to fact-checking with text, multimodal fact-checking relies on evidence to make judgments.
- 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 Dataset
- FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms (Qi et al., 2023) [Paper] [Dataset] AAAI 2023
- SciTweets - A Dataset and Annotation Framework for Detecting Scientific Online Discourse (Hafid et al., 2022) [Paper] [Dataset] CIKM 2022
- Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter (Sundriyal et al., 2022) [Paper] [Dataset] EMNLP 2022
- Stanceosaurus: Classifying Stance Towards Multilingual Misinformation (Zheng et al., 2022) [Paper] [Dataset] EMNLP 2022
- Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media (Park et al., 2022) [Paper] Findings EMNLP 2022
- CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets (Mohr et al., 2022) [Paper] [Dataset] LREC 2021
- MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset (Nielsen et al., 2022) [Paper] [Dataset] SIGIR 2021
- STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media (Rao et al., 2021) [Paper] [Dataset] EMNLP 2021
- 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] Findings EMNLP 2021
- Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection (Konstantinovskiy et al., 2021) [Paper] ACM Digital Threats: Research and Practice 2021
- 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] WWW 2021
- 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] ACL 2017
- A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates (Gencheva et al., 2017) [Paper] [Dataset] RANLP 2017
- Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs (Jin et al., 2017) [Paper] ACM MM 2017
- 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] IJCAI 2016
- Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads (Zubiaga et al., 2016). [Paper] [Dataset] PLOS One 2016
- CREDBANK: A Large-Scale Social Media Corpus with Associated Credibility Annotations (Mitra and Gilbert, 2015). [Paper] [Dataset] ICWSM 2015
- Detecting Check-worthy Factual Claims in Presidential Debates (Hassan et al., 2015) [Paper] CIKM 2015
Verdict Prediction Dataset
Veracity Classification Dataset
Natural Claims
- Modeling Information Change in Science Communication with Semantically Matched Paraphrases (Wright et al., 2022) [Paper] [Dataset] [Code] EMNLP 2022
- Generating Literal and Implied Subquestions to Fact-check Complex Claims (Chen et al., 2022) [Paper] [Dataset] EMNLP 2022
- CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking (Hu et al., 2022) [Paper] [Dataset] NAACL 2022
- WatClaimCheck: A new Dataset for Claim Entailment and Inference (Khan et al., 2022) [Paper] [Dataset] ACL 2022
- Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources (Abdelnabi et al., 2022) [Paper] [Dataset] CVPR 2022
- NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media (Luo et al., 2021) [Paper] [Dataset] EMNLP 2021
- Evidence-based Fact-Checking of Health-related Claims (Sarrouti et al., 2021) [Paper] [Dataset] Findings EMNLP 2021
- COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic (Saakyan et al., 2021) [Paper] [Dataset] ACL 2021
- InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection (Fung et al., 2021) [Paper] [Dataset] ACL 2021
- Edited Media Understanding Frames: Reasoning About the Intents and Implications of Visual Disinformation (Da et al., 2021) [Paper] [Code] ACL 2021
- Structurizing Misinformation Stories via Rationalizing Fact-Checks (Jiang et al., 2021) [Paper] [Dataset] ACL 2021
- X-FACT: A New Benchmark Dataset for Multilingual Fact Checking (Gupta and Srikumar, 2021) [Paper] [Dataset] ACL 2021
- LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification (Azevedo et al., 2021) [Paper] [Code] Findings ACL 2021
- Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection (Li et al., 2021) [Paper] Findings ACL 2021
- Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News (Tan et al., 2020) [Paper] [Dataset] EMNLP 2020
- Explainable Automated Fact-Checking for Public Health Claims (Kotonya and Toni, 2020b) [Paper] [Dataset] EMNLP 2020
- Fact or Fiction: Verifying Scientific Claims (Wadden et al., 2020). [Paper] [Dataset] EMNLP 2020
- AnswerFact: Fact Checking in Product Question Answering (Zhang et al., 2020) [Paper] [Dataset] EMNLP 2020
- Explainable Automated Fact-Checking for Public Health Claims (Kotonya and Toni, 2020). [Paper] [Dataset] EMNLP 2020
- r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection (Nakamura et al., 2020). [Paper] [Dataset] LREC 2020
- CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims (Diggelmann et al., 2020) [Paper] [Dataset] Workshop @ NeurIPS 2020
- FakeCovid-- A Multilingual Cross-domain Fact Check News Dataset for COVID-19 (Shahi and Nandini, 2020).
[Paper]
[Dataset]
ICWSM 2020 - 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] Big Data 2020
- A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking (Hanselowski et al., 2019). [Paper] [Code] [Dataset] CoNLL 2019
- MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims (Augenstein et al., 2019). [Paper] [Dataset] EMNLP 2019
- Fact-Checking Meets Fauxtography: Verifying Claims About Images (Zlatkova et al., 2019) [Paper] [Dataset] EMNLP 2019
- FA-KES: A Fake News Dataset around the Syrian War (Salem et al., 2019) [Paper] [Dataset] ICWSM 2019
- Fact Checking in Community Forums (Mihaylova et al., 2018) [Paper] [Dataset] AAAI 2018
- EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection [Paper] [Dataset] KDD 2018
- 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 News Veracity Dataset with Facebook User Commentary and Egos (Santia and Williams, 2018) [Paper]] [Dataset] ICWSM 2018
- A Stylometric Inquiry into Hyperpartisan and Fake News (Potthast et al., 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] EMNLP 2017
- “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection (Wang, 2017). [Paper] [Dataset] ACL 2017
- 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] NAACL 2016
- Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News (Rubin et al., 2016) [Paper] Workshop @ 2016
- Identification and Verification of Simple Claims about Statistical Properties (Vlachos and Riedel, 2015) [Paper] [Dataset] EMNLP 2015
- Fact Checking: Task definition and dataset construction (Vlachos and Riedel, 2014) [Paper] [Dataset] Workshop @ ACL 2014
- Verification and Implementation of Language-Based Deception Indicators in Civil and Criminal Narratives (Bachenko et al., 2008) [Paper]
Artificial Claims
- COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning (Aneja et al., 2023) [Paper] [Code] [Dataset] AAAI 2023
- Misinfo Reaction Frames: Reasoning about Readers’ Reactions to News Headlines (Gabriel et al., 2022) [Paper] [Dataset] ACL 2022
- DialFact: A Benchmark for Fact-Checking in Dialogue (Gupta et al., 2022) [Paper] [Dataset] ACL 2022
- FAVIQ: FAct Verification from Information-seeking Questions (Park et al., 2022) [Paper] [Dataset] ACL 2022
- FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information (Aly et al., 2021)
[Paper] [Dataset] [Code] NeurIPS 2021 - InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection (Fung et al., 2021) [Paper] [Dataset] ACL 2021
- 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] NAACL 2021
- 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]] NoDaLiDa 2021
- HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification (Jiang et al., 2020) [Paper] [Dataset] Findings EMNLP 2020
- INFOTABS: Inference on Tables as Semi-structured Data (Gupta et al., 2020) [Paper] [Dataset] ACL 2020
- TabFact: A Large-scale Dataset for Table-based Fact Verification (Chen et al., 2020) [Paper] [Dataset] ICLR 2020
- Unsupervised Fact Checking by Counter-Weighted Positive and Negative Evidential Paths in A Knowledge Graph (Kim and Choi, 2020) [Paper] COLING 2020
- 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]] NAACL 2018
- 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
Out-of-Context Classification Dataset
Shared Tasks
- The Fact Extraction and VERification (FEVER) Shared Task [5th FEVER Workshop]
- Statement Verification and Evidence Finding with Tables (SEM-TAB-FACT) [Wang et al., 2021]
- SciFact Claim Verifiation [Wadden et al., 2020]
- Fakeddit Multimodal Fake News Detection Challenge [Nakamura et al., 2020]
- SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours [Gorrell et al., 2019]
- SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums [Mihaylova et al., 2019]
- A Retrospective Analysis of the Fake News Challenge Stance-Detection Task [Hanselowski et al., 2018]
- The Fact Extraction and VERification (FEVER) Shared Task [Thorne et al., 2018]
- SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours [Derczynski et al., 2017]
- The Fake News Challenge (FNC-1) [Pomerleau and Rao, 2017]
Models
Claim Detection
- Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning (Lin et al., 2023) [Paper] AAAI 2023
- Unsupervised Cross-Domain Rumor Detection with Contrastive Learning and Cross-Attention (Ran et al., 2023) [Paper] AAAI 2023
- Zoom Out and Observe: News Environment Perception for Fake News Detection (Sheng et al., 2022) [Paper] [Code] ACL 2022
- DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media (Sun et al., 2022) [Paper] AAAI 2022
- Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks (Lin et al., 2021) [Paper] EMNLP 2021
- STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media (Rao et al., 2021) [Paper] [Code] EMNLP 2021
- Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection (Sun et al., 2021) [Paper] [Code] Findings EMNLP 2021
- Active Learning for Rumor Identification on Social Media (Farinneya et al., 2021) [Paper] Findings EMNLP 2021
- Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detection (Wei et al., 2021) [Paper] [Code] ACL 2021
- Adversary-Aware Rumor Detection (Song et al., 2021) [Paper] [Code] Findings ACL 2021
- Learning Disentangled Latent Topics for Twitter Rumour Veracity Classification (Dougrez-Lewis et al., 2021) [Paper] [Code] Findings ACL 2021
- Mining Dual Emotion for Fake News Detection (Zhang et al., 2021). [Paper] [Code] WWW 2021
- Claim Check-Worthiness Detection as Positive Unlabelled Learning (Wright and Augenstein, 2021) [Paper] [Code] Findings EMNLP 2020
- Exploiting Microblog Conversation Structures to Detect Rumors (Li et al., 2020). [Paper] COLING 2020
- Debunking Rumors on Twitter with Tree Transformer (Ma et al., 2020) [Paper] COLING 2020
- VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text (Cheng et al., 2020) [Paper] [Code] WWW 2020
- Rumor Detection on Social Media with Graph Structured Adversarial Learning (Yang et al., 2020) [Paper] IJCAI 2020
- 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] AAAI 2020
- Fake News Early Detection: A Theory-driven Model (Zhou et al., 2020). [Paper] AAAI 2020
- MVAE: Multimodal Variational Autoencoder for Fake News Detection (Khattar et al., 2019). [Paper] [Code] WWW 2019
- 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] ACL 2018
- Rumor Detection with Hierarchical Social Attention Network (Guo et al., 2018). [Paper] CIKM 2018
- 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] RANLP 2017
- 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] IJCAI 2016
Verdict Prediction
Veractiy Classification
- Bootstrapping Multi-view Representations for Fake News Detection (Ying et al., 2023) [Paper] AAAI 2023
- Varifocal Question Generation for Fact-checking (Ousidhoum et al., 2022) [Paper] EMNLP 2022
- ProoFVer: Natural Logic Theorem Proving for Fact Verification (Krishna et al., 2022) [Paper] TACL 2022
- MultiVerS: Improving scientific claim verification with weak supervision and full-document context (Wadden et al., 2022) [Paper] [Code] Findings NAACL 2022
- Generating Scientific Claims for Zero-Shot Scientific Fact Checking (Wright et al., 2022) [Paper] [Code] ACL 2022
- Automatic Detection of Entity-Manipulated Text Using Factual Knowledge (Jawahar et al., 2022) [Paper] [Code] ACL 2022
- LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification (Chen et al., 2022) [Paper] [Code] AAAI 2022
- Towards Fine-Grained Reasoning for Fake News Detection (Jin et al., 2022) [Paper] AAAI 2022
- Synthetic Disinformation Attacks on Automated Fact Verification Systems (Du et al., 2021) [Paper] [Code] AAAI 2022
- Editing Factual Knowledge in Language Models (De Cao et al., 2021) [Paper] [Code] EMNLP 2021
- Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification (Shi et al., 2021) [Paper] [Code] EMNLP 2021
- Students Who Study Together Learn Better: On the Importance of Collective Knowledge Distillation for Domain Transfer in Fact Verification (Mithun et al., 2021) [Paper] EMNLP 2021
- Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification (Zhang et al., 2021) [Paper] [Code] EMNLP 2021
- Table-based Fact Verification with Salience-aware Learning (Wang et al., 2021) [Paper] [Code] Findings EMNLP 2021
- Exploring Decomposition for Table-based Fact Verification (Yang et al., 2021) [Paper] [Code] Findings EMNLP 2021
- Joint Verification and Reranking for Open Fact Checking Over Tables (Schlichtkrull et al., 2021). [Paper] [Code] ACL 2021
- Multi-Task Retrieval for Knowledge-Intensive Tasks (Maillard et al., 2021). [Paper] ACL 2021
- Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification (Si et al., 2021). [Paper] [Code] ACL 2021
- A DQN-based Approach to Finding Precise Evidences for Fact Verification (Wan et al., 2021) [Paper] [Code] ACL 2021
- Unified Dual-view Cognitive Model for Interpretable Claim Verification (Wu et al., 2021) [Paper] ACL 2021
- Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge (Hu et al., 2021) [Paper] [Code] ACL 2021
- Automatic Fake News Detection: Are Models Learning to Reason? (Hansen et al., 2021) [Paper] [Code] ACL 2021
- Exploring Listwise Evidence Reasoning with T5 for Fact Verification (Jiang et al., 2021) [Paper] ACL 2021
- Multimodal Fusion with Co-Attention Networks for Fake News Detection (Wu et al., 2021)
[Paper]
Findings ACL 2021 - A Multi-Level Attention Model for Evidence-Based Fact Checking (Kruengkrai et al., 2021) [Paper] [Code] Findings ACL 2021
- Strong and Light Baseline Models for Fact-Checking Joint Inference (Tymoshenko et al., 2021) [Paper] [Code] Findings ACL 2021
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020). [Paper] [Code] NeurIPS 2021
- 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] EMNLP 2020
- Understanding tables with intermediate pre-training (Eisenschlos et al., 2020). [Paper] [Code] Findings EMNLP 2020
- Fine-grained Fact Verification with Kernel Graph Attention Network (Liu et al., 2020). [Paper] [Code] ACL 2020
- 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] ACL 2020
- Scrutinizer: A Mixed-Initiative Approach to Large-Scale, Data-Driven Claim Verification (Karagiannis et al., 2020) [Paper] [Code] VLDB 2020
- 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]]
- Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks (Ma et al., 2019). [Paper]
- 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] ECIR 2019
- 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
- “Why is this misleading?”: Detecting News Headline Hallucinations with Explanations (Shen et al., 2023) [[Paper]]( [Paper]] AAAI 2023)] WWW 2023
- Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning (Si et al., 2023) [Paper]] AAAI 2023
- Explainable Automated Fact-Checking for Public Health Claims (Kotonya and Toni, 2020). [Paper]] [Code] [Dataset] EMNLP 2020
- Generating Fact Checking Explanations (Atanasova et al., 2020). [Paper] ACL 2020
- GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (Lu and Li, 2020). [Paper] [Code] ACL 2020
- DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (Wu et al., 2020). [Paper] ACL 2020
- 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
Misinformation and Disinformation
- Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation (He et al., 2023) [Paper] [Dataset] [Code] WWW 2023
- Who Funds Misinformation? A Systematic Analysis of the Ad-related Profit Routines of Fake News sites (Papadogiannakis et al., 2023) [Paper] WWW 2023
- A Survey on Multimodal Disinformation Detection (Alam et al., 2021) [Paper]
- Misinformation, Disinformation, and Online Propaganda (Guess and Lyons, 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]
Detecting Previous Claims
- Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document (Shaar et al., 2022) [Paper] Findings EMNLP 2022
- Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims (Sheng et al. 2021) [Paper] [Code] ACL 2021
- Claim Matching Beyond English to Scale Global Fact-Checking (Kazemiet al. 2021) [Paper] **ACL 2021
- 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] ACL 2020
- 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]
Relevant Surveys
Automated Fact-Checking
- 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]
- 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
- Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era [Nakov and Da San Martino, EMNLP 2020].
- Detection and Resolution of Rumors and Misinformation with NLP [Derczynski and Zubiaga, COLING 2020] [slides].
- Fact Checking: Theory and Practice [Dong et al., KDD 2018].