新任务MisLC评估假信息的法律后果，推动跨领域应对社会危害。
Misinformation with Legal Consequences (MisLC): A New Task Towards Harnessing Societal Harm of Misinformation
- 以法律问题定义假信息的社会影响，构建多领域法律框架。
- 通过众包与专家评估，建立包含4大类11小类的标注数据集。
- 大模型仍难超人类专家，适合法律与舆情交叉研究者参考。
假信息指虚假或不准确的信息，无论出于恶意还是无心，传播后可能引发重大社会危害。快速的在线信息传播要求更先进的检测机制以减轻假信息带来的损害。现有研究多聚焦于信息真实性判断，忽视了假信息的法律后果与社会影响。本文提出一项新任务——具有法律后果的假信息（MisLC），以多领域法律定义为依据，涵盖4个广义法律主题和11个细粒度法律问题，包括仇恨言论、选举法、隐私保护等。为此，我们采用两步式数据集构建方法：先通过众包评估信息可信度，再由专家评审假信息性质。实证研究从任务定义、实验分析到专家参与提供深入洞察。尽管当前大型语言模型与检索增强生成技术可作为有效基线，但仍远未达到专家水平。
原文摘要 · Abstract (English)
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.
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