mlsanityData to 14 Oct 2026

Misinformation with Legal Consequences (MisLC): A New Task Towards Harnessing Societal Harm of Misinformation

arXiv 2410.03829 ↗First posted 4 Oct 2024cs.CLcited by 1

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Abstract (arXiv)

Misinformation, defined as false or inaccurate information, can result in significant societal harm when it is spread with malicious or even innocuous intent. The rapid online information exchange necessitates advanced detection mechanisms to mitigate misinformation-induced harm. Existing research, however, has predominantly focused on assessing veracity, overlooking the legal implications and social consequences of misinformation. In this work, we take a novel angle to consolidate the definition of misinformation detection using legal issues as a measurement of societal ramifications, aiming to bring interdisciplinary efforts to tackle misinformation and its consequence. We introduce a new task: Misinformation with Legal Consequence (MisLC), which leverages definitions from a wide range of legal domains covering 4 broader legal topics and 11 fine-grained legal issues, including hate speech, election laws, and privacy regulations. For this task, we advocate a two-step dataset curation approach that utilizes crowd-sourced checkworthiness and expert evaluations of misinformation. We provide insights about the MisLC task through empirical evidence, from the problem definition to experiments and expert involvement. While the latest large language models and retrieval-augmented generation are effective baselines for the task, we find they are still far from replicating expert performance.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 47 references that are in the map. First posted on arXiv 4 Oct 2024.

Builds on

22 of 47 references.

References · 22

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10 more
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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Towards Low-Resource Alignment to Diverse Perspectives with Sparse Feedbackcited by 1

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  6. 2026Wisdom of the LLM Crowd: A Large Scale Benchmark of Multi-Label U.S. Election-Related Harmful Social Media Content
  7. 2024On the Risk of Evidence Pollution for Malicious Social Text Detection in the Era of LLMs
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