See ACL Anthology - Automatic Fact Checking.

• Thorne & Vlachos 2018 - Automated Fact Checking: Task Formulations, Methods and Future Directions
• Popular media: 2019 - Understanding the Promise and Limits of Automated Fact-Checking
• Kotonya & Toni 2020 - Explainable Automated Fact-Checking: A Survey
• Das et al 2023 - The State of Human-centered NLP Technology for Fact-checking

• Thorne et al 2018 - FEVER: A Large-Scale Dataset for Fact Extraction and VERification
• Fact checking against tables: Chen at el 2019 - TabFact: A Large-scale Dataset for Table-based Fact Verification
  • Zhong et al 2020 - LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network
  • Ou & Liu 2022 - Learning to Generate Programs for Table Fact Verification via Structure-Aware Semantic Parsing
• Claims verification
  • Khan et al 2022 - WatClaimCheck: A new Dataset for Claim Entailment and Inference

• Overviews
  • Oshikawa et al 2018 - A Survey on Natural Language Processing for Fake News Detection
• GROVER: Zellers et al 2019 - Defending Against Neural Fake News
• Nakamura et al 2019 - Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
• Fung et al 2021 - InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection

• In Language Models
  • Lee et al 2022 - Factuality Enhanced Language Models for Open-Ended Text Generation

• FEVER: Paper, GitHub

• Workshop on Fact Extraction and Verification (FEVER)

• Preslav Nakov
• Andreas Vlachos