@inproceedings{zhang-etal-2025-spark,
title = "{SPARK}: Simulating the Co-evolution of Stance and Topic Dynamics in Online Discourse with {LLM}-based Agents",
author = "Zhang, Bowen and
Yang, Yi and
Niu, Fuqiang and
Fu, Xianghua and
Dai, Genan and
Huang, Hu",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1176/",
doi = "10.18653/v1/2025.emnlp-main.1176",
pages = "23061--23073",
ISBN = "979-8-89176-332-6",
abstract = "Topic evolution and stance dynamics are deeply intertwined in online social media, shaping the fragmentation and polarization of public discourse. Yet existing dynamic topic models and stance analysis approaches usually consider these processes in isolation, relying on abstractions that lack interpretability and agent-level behavioral fidelity. We present stance and topic evolution reasoning framework (SPARK), the first LLM-based multi-agent simulation framework for jointly modeling the co-evolution of topics and stances through natural language interactions. In SPARK, each agent is instantiated as an LLM persona with unique demographic and psychological traits, equipped with memory and reflective reasoning. Agents engage in daily conversations, adapt their stances, and organically introduce emergent subtopics, enabling interpretable, fine-grained simulation of discourse dynamics at scale. Experiments across five real-world domains show that SPARK captures key empirical patterns{---}such as rapid topic innovation in technology, domain-specific stance polarization, and the influence of personality on stance shifts and topic emergence. Our framework quantitatively reveals the bidirectional mechanisms by which stance shifts and topic evolution reinforce each other, a phenomenon rarely addressed in prior work. SPARK provides actionable insights and a scalable tool for understanding and mitigating polarization in online discourse. Code and simulation resources will be released after acceptance."
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<abstract>Topic evolution and stance dynamics are deeply intertwined in online social media, shaping the fragmentation and polarization of public discourse. Yet existing dynamic topic models and stance analysis approaches usually consider these processes in isolation, relying on abstractions that lack interpretability and agent-level behavioral fidelity. We present stance and topic evolution reasoning framework (SPARK), the first LLM-based multi-agent simulation framework for jointly modeling the co-evolution of topics and stances through natural language interactions. In SPARK, each agent is instantiated as an LLM persona with unique demographic and psychological traits, equipped with memory and reflective reasoning. Agents engage in daily conversations, adapt their stances, and organically introduce emergent subtopics, enabling interpretable, fine-grained simulation of discourse dynamics at scale. Experiments across five real-world domains show that SPARK captures key empirical patterns—such as rapid topic innovation in technology, domain-specific stance polarization, and the influence of personality on stance shifts and topic emergence. Our framework quantitatively reveals the bidirectional mechanisms by which stance shifts and topic evolution reinforce each other, a phenomenon rarely addressed in prior work. SPARK provides actionable insights and a scalable tool for understanding and mitigating polarization in online discourse. Code and simulation resources will be released after acceptance.</abstract>
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%0 Conference Proceedings
%T SPARK: Simulating the Co-evolution of Stance and Topic Dynamics in Online Discourse with LLM-based Agents
%A Zhang, Bowen
%A Yang, Yi
%A Niu, Fuqiang
%A Fu, Xianghua
%A Dai, Genan
%A Huang, Hu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhang-etal-2025-spark
%X Topic evolution and stance dynamics are deeply intertwined in online social media, shaping the fragmentation and polarization of public discourse. Yet existing dynamic topic models and stance analysis approaches usually consider these processes in isolation, relying on abstractions that lack interpretability and agent-level behavioral fidelity. We present stance and topic evolution reasoning framework (SPARK), the first LLM-based multi-agent simulation framework for jointly modeling the co-evolution of topics and stances through natural language interactions. In SPARK, each agent is instantiated as an LLM persona with unique demographic and psychological traits, equipped with memory and reflective reasoning. Agents engage in daily conversations, adapt their stances, and organically introduce emergent subtopics, enabling interpretable, fine-grained simulation of discourse dynamics at scale. Experiments across five real-world domains show that SPARK captures key empirical patterns—such as rapid topic innovation in technology, domain-specific stance polarization, and the influence of personality on stance shifts and topic emergence. Our framework quantitatively reveals the bidirectional mechanisms by which stance shifts and topic evolution reinforce each other, a phenomenon rarely addressed in prior work. SPARK provides actionable insights and a scalable tool for understanding and mitigating polarization in online discourse. Code and simulation resources will be released after acceptance.
%R 10.18653/v1/2025.emnlp-main.1176
%U https://aclanthology.org/2025.emnlp-main.1176/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1176
%P 23061-23073
Markdown (Informal)
[SPARK: Simulating the Co-evolution of Stance and Topic Dynamics in Online Discourse with LLM-based Agents](https://aclanthology.org/2025.emnlp-main.1176/) (Zhang et al., EMNLP 2025)
ACL