Preventing Accidental Sharing of Misinformation Using Large Language Models

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.

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Publication Details

Published
2025-09-03
DOI
https://doi.org/10.1145/3748699.3749798
Primary Topic
Misinformation and Its Impacts
Type
article
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Preventing Accidental Sharing of Misinformation Using Large Language Models

Misinformation and Its Impacts
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Preventing Accidental Sharing of Misinformation Using Large Language Models

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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.

University of Padua (IT), Utrecht University (NL), Ostwestfalen-Lippe University of Applied Sciences and Arts (DE)
Quality Education
Openalex Percentile: Top 27%
Misinformation and Its Impacts
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Preventing Accidental Sharing of Misinformation Using Large Language Models · (2025) | TGRS Research Map | TGRS