@inproceedings{zhang-etal-2025-trendsim,
    title = "{T}rend{S}im: Simulating Trending Topics in Social Media Under Poisoning Attacks with {LLM}-based Multi-agent System",
    author = "Zhang, Zeyu  and
      Lian, Jianxun  and
      Ma, Chen  and
      Qu, Yaning  and
      Luo, Ye  and
      Wang, Lei  and
      Li, Rui  and
      Chen, Xu  and
      Lin, Yankai  and
      Wu, Le  and
      Xie, Xing  and
      Wen, Ji-Rong",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-naacl.160/",
    pages = "2930--2949",
    ISBN = "979-8-89176-195-7",
    abstract = "Trending topics have become a significant part of modern social media, attracting users to participate in discussions of breaking events. However, they also bring in a new channel for poisoning attacks, resulting in negative impacts on society. Therefore, it is urgent to study this critical problem and develop effective strategies for defense. In this paper, we propose TrendSim, an LLM-based multi-agent system to simulate trending topics in social media under poisoning attacks. Specifically, we create a simulation environment for trending topics that incorporates a time-aware interaction mechanism, centralized message dissemination, and an interactive system. Moreover, we develop LLM-based humanoid agents to simulate users in social media, and propose prototype-based attackers to replicate poisoning attacks. Besides, we evaluate TrendSim from multiple aspects to validate its effectiveness. Based on TrendSim, we conduct simulation experiments to study four critical problems about poisoning attacks on trending topics."
}