A Survey on Automated Fact-Checking

• DOI: 10.48550/arxiv.2108.11896
• Publication Date: 2022-01-01
• Authors: Guo, Zhijiang; Schlichtkrull, Mi...; Vlachos, Andreas
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; 02 engineering and technology; 01 natural sciences; Networking and Information Technology R&D (NITRD); 46 Information and Computing Sciences; 4602 Artificial Intelligence; Computational linguistics. Natural language processing; Machine Learning and Artificial Intelligence; 0202 electrical engineering, electronic engineering, information engineering; Networking and Information Technology R&D (NITRD); P98-98.5; Computation and Language (cs.CL); 0105 earth and related environmental sciences

Abstract Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem. Therefore, researchers have been exploring how fact-checking can be automated, using techniques based on natural language processing, machine learning, knowledge representation, and databases to automatically predict the veracity of claims. In this paper, we survey automated fact-checking stemming from natural language processing, and discuss its connections to related tasks and disciplines. In this process, we present an overview of existing datasets and models, aiming to unify the various definitions given and identify common concepts. Finally, we highlight challenges for future research.