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Automated Fact-Checking for Assisting Human Fact-Checkers

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arxiv 2103.07769 v2 pith:FYXLUZMJ submitted 2021-03-13 cs.AI cs.CLcs.CRcs.IRcs.LG

classification cs.AIcs.CLcs.CRcs.IRcs.LG
keywords claimsfact-checkingavailablefact-checkermediaprofessionalclaimdirect
verification ladder T0 review T1 audit T2 compute T3 formal
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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. As in other text forensics tasks, the amount of information available makes the work of the fact-checker more difficult. With this in mind, starting from the perspective of the professional fact-checker, 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 in future work and the potential impact on real-world fact-checking.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking

    cs.HC 2025-02 conditional novelty 7.0 of 10

    Fact-checkers want automated fact-checking explanations that trace the reasoning path, cite checkable evidence, and clearly flag uncertainty and information gaps, not just confidence scores.

  2. CANDY: Benchmarking LLMs' Limitations and Assistive Potential in Chinese Misinformation Fact-Checking

    cs.CL 2025-09 conditional novelty 5.0 of 10

    CANDY, a Chinese misinformation fact-checking benchmark, shows LLMs reach only ~76% accuracy on contamination-free claims and frequently fabricate supporting evidence, while serving better as human assistants than aut...

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