Automated Fact-Checking for Assisting Human Fact-Checkers

By Preslav Nakov, David P. A. Corney, Maram Hasanain, Firoj Alam, Tamer Elsayed, Alberto Barrón-Cedeño, Paolo Papotti, Shaden Shaar, Giovanni Da San Martino. 2021

316 citations recorded by the source corpus.

The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism. Nowadays, politicians and other key players enjoy direct access to their audiences through social media, bypassing the filters of official cables or traditional media. However, the multiple advantages of free speech and direct communication are dimmed by the misuse of media to spread inaccurate or misleading claims. These phenomena have led to the modern incarnation of the fact-checker --- a professional whose main aim is to examine claims using available evidence and to assess their veracity. Here, we survey the available intelligent technologies that can support the human expert in the different steps of her fact-checking endeavor. These include identifying claims worth fact-checking, detecting relevant previously fact-checked claims, retrieving relevant evidence to fact-check a claim, and actually verifying a claim. In each case, we pay attention to the challenges and the potential impact on real-world fact-checking.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science, Natural Language Processing

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Citations in the corpus

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, background): “These approaches have also been adopted to assist human fact-checkers (Shaar et al., 2021; Nakov et al., 2021).”
Style Over Substance: Evaluation Biases for Large Language Models (2023, unknown): “Relatedly, instances are found where humans over-looked basic verification tasks, like identifying fake URLs or citations (Nakov et al., 2021), 7 .”
Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document (2021, background): “More relevant to the present work, Nakov et al. (2021a) studied how AI technology can assist professional fact-checkers, and pointed to the following research problems: ( i ) identifying claims worth fact-checking, ( ii ) detecting relevant previously fact-checked claims, ( iii ) retrieving…”
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information (2021, background): “…misleading and false claims rises [Graves, 2018], resulting in the development of both fully automated methods (see Thorne and Vlachos [2018], Zubiaga et al. [2018], Hardalov et al. [2021] for recent surveys) as well as technologies that can assist human journalists [Nakov et al., 2021].”
WikiFactFind: Semi-automated fact-checking based on Wikipedia (2022, background): “At the same time, even if full automation remains unreachable, tools that support fact-checkers in their manual work are to be welcomed [10].”