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    <abstract>Large language models are increasingly used to simulate social media users and infer how individuals may respond to online discussions. However, it remains unclear whether these simulations reflect precise user-specific beliefs or whether they are highly sensitive to semantically independent changes in conversational contexts. In this work, we study counterfactual context revision as a framework for auditing LLM-based stance simulation. Given an original online conversation, we first infer a target user’s stance toward a specific topic. We then apply controlled revision strategies to the conversational context and simulate the user’s stance again under the revised context. We compare text-only revision strategies with a multimodal one that incorporates meme-based context and evaluate two main effectiveness metrics, i.e., average directional stance shift and stance transition rate. The results reveal effective and robust stance transitions in both text-only and multimodal strategies across different polarization-preference mechanisms. Our study contributes an evaluation framework for understanding the context sensitivity of LLM-based stance simulation (https://github.com/STARResearchLab/StanceShift). More broadly, it highlights both the promise and risk of using LLMs as proxies for social media users in social simulation.</abstract>
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%0 Conference Proceedings
%T Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions
%A Zhang, Xinnong
%A Shan, Wanting
%A Lyu, Hanjia
%A Wei, Zhongyu
%A Luo, Jiebo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-revising
%X Large language models are increasingly used to simulate social media users and infer how individuals may respond to online discussions. However, it remains unclear whether these simulations reflect precise user-specific beliefs or whether they are highly sensitive to semantically independent changes in conversational contexts. In this work, we study counterfactual context revision as a framework for auditing LLM-based stance simulation. Given an original online conversation, we first infer a target user’s stance toward a specific topic. We then apply controlled revision strategies to the conversational context and simulate the user’s stance again under the revised context. We compare text-only revision strategies with a multimodal one that incorporates meme-based context and evaluate two main effectiveness metrics, i.e., average directional stance shift and stance transition rate. The results reveal effective and robust stance transitions in both text-only and multimodal strategies across different polarization-preference mechanisms. Our study contributes an evaluation framework for understanding the context sensitivity of LLM-based stance simulation (https://github.com/STARResearchLab/StanceShift). More broadly, it highlights both the promise and risk of using LLMs as proxies for social media users in social simulation.
%U https://aclanthology.org/2026.findings-emnlp.1600/
%P 34088-34107

Markdown (Informal)

[Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions](https://aclanthology.org/2026.findings-emnlp.1600/) (Zhang et al., Findings 2026)

• Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions (Zhang et al., Findings 2026)

ACL

• Xinnong Zhang, Wanting Shan, Hanjia Lyu, Zhongyu Wei, and Jiebo Luo. 2026. Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 34088–34107, Budapest, Hungary. Association for Computational Linguistics.