@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://aclanthology.org/2025.findings-naacl.160/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System
%A Zhang, Zeyu
%A Lian, Jianxun
%A Ma, Chen
%A Qu, Yaning
%A Luo, Ye
%A Wang, Lei
%A Li, Rui
%A Chen, Xu
%A Lin, Yankai
%A Wu, Le
%A Xie, Xing
%A Wen, Ji-Rong
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F zhang-etal-2025-trendsim
%X 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.
%R 10.18653/v1/2025.findings-naacl.160
%U https://aclanthology.org/2025.findings-naacl.160/
%U https://doi.org/10.18653/v1/2025.findings-naacl.160
%P 2930-2949
Markdown (Informal)
[TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System](https://aclanthology.org/2025.findings-naacl.160/) (Zhang et al., Findings 2025)
ACL
- Zeyu Zhang, Jianxun Lian, Chen Ma, Yaning Qu, Ye Luo, Lei Wang, Rui Li, Xu Chen, Yankai Lin, Le Wu, Xing Xie, and Ji-Rong Wen. 2025. TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 2930–2949, Albuquerque, New Mexico. Association for Computational Linguistics.