Automated Fact-Checking Resources

Overview

This repo contains relevant resources from our survey paper A Survey on Automated Fact-Checking accepted by TACL 2021. In this survey, we present a comprehensive and up-to-date survey of automated fact-checking, 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
Datasets
- Claim Detection
- Factual Verification
  - Natural Claims
  - Artificial Claims
Shared Tasks
Models
Relevant Surveys
Related Tasks
- Misinformation and Disinformation
- Detecting Previous Claims
Tutorials

Task Definition

Figure below shows a NLP framework for automated fact-checking 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.

Datasets

Claim Detection Dataset

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

Factual Verification Dataset

Natural Claims

Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources (Abdelnabi et al., 2022) [Paper] [Dataset]
NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media (Luo et al., 2022) [Paper] [Dataset]
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
Fact or Fiction: Verifying Scientific Claims (Wadden 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

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]

Shared Tasks

The Fact Extraction and VERification (FEVER) Shared Task [4th FEVER Workshop] The shared task is ongoing!
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

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]
Fake News Early Detection: A Theory-driven Model (Zhou et al., 2020). [Paper]
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] 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]

Factual Verification

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
ProoFVer: Natural Logic Theorem Proving for Fact Verification (Krishna et al., 2021) [Paper]
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]

Justification Production

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

Misinformation and Disinformation

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

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]

Relevant Surveys