Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation

• URL: http://arxiv.org/abs/2506.07211v1
• Date: Sun, 08 Jun 2025 16:24:11 GMT
• Title: Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation
• Authors: Gionnieve Lim, Bryan Chen Zhengyu Tan, Kellie Yu Hui Sim, Weiyan Shi, Ming Hui Chew, Ming Shan Hee, Roy Ka-Wei Lee, Simon T. Perrault, Kenny Tsu Wei Choo,
• Abstract summary: Large Language Models (LLMs) can be weaponised to produce sophisticated and persuasive disinformation, yet they also hold promise for enhancing detection and mitigation strategies.<n>This paper investigates the complex dynamics between LLMs and disinformation through a communication game that simulates online forums, inspired by the game Werewolf, with 25 participants.<n>Our findings highlight the varying uses of LLMs depending on the participants' roles and strategies, underscoring the importance of understanding their effectiveness in this context.
• Score: 9.761926423405617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emergence of Large Language Models (LLMs) presents a dual challenge in the fight against disinformation. These powerful tools, capable of generating human-like text at scale, can be weaponised to produce sophisticated and persuasive disinformation, yet they also hold promise for enhancing detection and mitigation strategies. This paper investigates the complex dynamics between LLMs and disinformation through a communication game that simulates online forums, inspired by the game Werewolf, with 25 participants. We analyse how Disinformers, Moderators, and Users leverage LLMs to advance their goals, revealing both the potential for misuse and combating disinformation. Our findings highlight the varying uses of LLMs depending on the participants' roles and strategies, underscoring the importance of understanding their effectiveness in this context. We conclude by discussing implications for future LLM development and online platform design, advocating for a balanced approach that empowers users and fosters trust while mitigating the risks of LLM-assisted disinformation.

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