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. More broadly, it highlights both the promise and risk of using LLMs to simulate online opinion dynamics.

翻译：大型语言模型越来越多地被用于模拟社交媒体用户，并推断用户在在线讨论中的可能反应。然而，目前仍不清楚这些模拟反映的是用户特定的精确信念，还是对对话语境中语义无关的变化高度敏感。本研究将反事实语境修正作为审计基于LLM的立场模拟的框架。给定原始在线对话，我们首先推断目标用户在特定话题上的立场。然后，我们对对话语境应用受控修正策略，并在修正后的语境下再次模拟用户的立场。我们比较了纯文本修正策略与结合了模因语境的多模态策略，并评估了两个主要有效性指标，即平均定向立场偏移和立场转变率。结果揭示了在文本策略和多模态策略中，不同极化偏好机制下均存在有效且稳健的立场转变。本研究为理解基于LLM的立场模拟的语境敏感性提供了一个评估框架。更广泛而言，它突显了使用LLM模拟在线舆论动态的前景与风险。