Misinformation with Legal Consequences ( M is LC ): A New Task Towards Harnessing Societal Harm of Misinformation (2024)

Abstract: Misinformation, defined as false or inaccurate information, can result in significant societal harm when it is spread with malicious or even unintentional intent. The rapid online information exchange necessitates advanced detection mechanisms to mitigate misinformation-induced harm. Existing research, however, has predominantly focused on the veracity of information, overlooking the legal implications and 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.

## introduction artificial intelligence is advancing with an unprecedented speed, and many emerging problems with profound societal impact need multi-disciplinary research efforts and solutions. misinformation, broadly defined as false or inaccurate information, has had a widespread harmful impact. if unaddressed, it will persist and exacerbate systemic 1 our code and data are available at https://github. com/chufeiluo/mislc for replicability. problems in our daily life as well as many critical areas @xcite . for instance, conflicting information during the covid-19 pandemic significantly influenced people's attitudes and behaviours toward preventing viral spread @xcite . in significant economic or political events, misinformation also proves extremely detrimental (e.g., in the form of fake news), where malicious actors are motivated to purposely spread false information to manipulate public opinion while an event unfolds @xcite . the growing menace of online misinformation underscores the urgent need for regulation, as exemplified by the european commission's recent action plans. 2 we believe that nlp enabled solutions will play a critical role in mitigating the adversarial affects of misinformation. these solutions require a human-centric approach, with the basic design to ensure the alignment between humans and ai, centring on the values and interests of humans @xcite @xcite . collaborations with experts in the social sciences are essential to achieve this goal. in this work, we take a novel angle to define misinformation with its outcome that can be regularized by laws or regulations, building on legal issues as a measurement of societal ramifications, and aiming to bring interdisciplinary efforts to tackle misinformation and its consequence. unlike previous work that has focused on factual accuracy or checkworthiness as potential controversy of a topic @xcite , we ground our definition in legal literature and social consequence. our main contributions are summarized as follows: after thorough empirical study, we find the existing llms perform reasonably well at the task, achieving non-random performance without external resources. their performance also increases consistently with rag. however, llms are still far from matching human expert performance. through this work, we urge further exploration in this challenging task with significant societal impact. ## related work misinformation is a serious issue with significant societal impact, as factual dissonance can cause disorder in peoples' worldviews @xcite . there have been various works that address separate components of the fact-checking pipeline: identifying checkworthy claims, gathering sources on those claims, and predicting veracity @xcite . there is growing interest in addressing the problem with llms @xcite , and emerging works proposing new methodologies for fact-checking @xcite . however, these works do not consider the legal concept of misinformation. generative, or auto-regressive models have recently demonstrated strong proficiency in a wide variety of tasks such as relevance, stance, topics, and frame detection in tweets @xcite . large language models (llms) have also demonstrated the ability to capture and memorize a vast amount of world knowledge during pretraining @xcite . however, this knowledge is stored implicitly within their parameters, leading to a lack of transparency for the facts and information generated in their outputs @xcite . one viable strategy for factual accuracy is giving explicit knowledge from external corpora, or retrieval-augmented generation @xcite . some approaches prepend retrieved documents in the input @xcite @xcite . for further related work, please refer to appendix a. ## experiment results we perform experiments on a wide range of publicly available llms. considering its importance for the legal domain and our task here, we extend our investigation to include retrieval augmented generation (rag). ## conclusion we introduce a new task: misinformation with legal consequence (mislc) built on a body of literature spanning 4 broader legal topics and 11 finegrained legal issues. a comprehensive study is performed on a wide range of open-source and proprietary llms that covers a broad parameter spectrum and varying training data. we also adapt existing works in retrieval-augmented generation (rag), retrieving from the web as well as our curated body of legal documents. we show the task remains challenging for the existing state-of-the-art large language models, even with the use of rag. we also demonstrate trends from general domain tasks, such as a higher frequency of retrieval negatively impacting performance, is also reflected in performance on our specialized legal dataset. we hope our work can enable future research on this important task with significant societal impact.