Preventing Accidental Sharing of Misinformation Using Large Language Models

Publication date

2025-12-09

Authors

Franco, Mirko
Herder, EelcoISNI 0000000390494456
Grimm, Valentin

Editors

Advisors

Supervisors

Document Type

Part of book
Open Access logo

License

cc_by

Abstract

The proliferation of misinformation is one of the most pressing challenges in today's digital landscape, due to its far-reaching implications for public health, economic stability, trust in governmental institutions, and societal cohesion. Despite efforts to regulate online platforms and limit the spread of misinformation, many individuals are left behind because of their low digital literacy, level of education, and other contributing factors. In this context, we explore the use of Large Language Models (LLMs) to identify misinformation and we evaluate the capabilities of GPT-4.1-mini, as a representative example of these models. We then discuss how LLMs can help empower users to critically create and share information, thereby fostering more resilient online communities. We also present a set of possible interaction patterns for content creation and moderation.

Keywords

fake news, large language models, misinformation, online social networks, Computer Networks and Communications, Information Systems, SDG 3 - Good Health and Well-being

Citation

Franco, M, Herder, E & Grimm, V 2025, Preventing Accidental Sharing of Misinformation Using Large Language Models. in GoodIT 2025 - Proceedings of the 2025 International Conference on Information Technology for Social Good. GoodIT 2025 - Proceedings of the 2025 International Conference on Information Technology for Social Good, Association for Computing Machinery, pp. 244-252, 5th International Conference on Information Technology for Social Good, GoodIT 2025, Antwerp, Belgium, 3/09/25. https://doi.org/10.1145/3748699.3749798, conference