aiqbench analysis
This study introduces a counterfactual context revision framework to audit the robustness of LLM-based stance simulation, revealing that simulated opinions are highly sensitive to both text-only and multimodal context changes. By quantifying stance shifts through directional metrics, the work establishes a critical evaluation baseline for understanding the reliability of models used to predict online opinion dynamics. Ultimately, it underscores the dual potential and inherent risks of deploying LLMs to simulate complex social media interactions without rigorous context stability checks.
Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions
arXiv:2606.06443v2 Announce Type: replace 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. More broadly, it highlights both the promise and risk of using LLMs to simulate online opinion dynamics.
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. More broadly, it highlights both the promise and risk of using LLMs to simulate online opinion dynamics.
Computation and Language (cs.CL); Multimedia (cs.MM); Social and Information Networks (cs.SI)
arXiv:2606.06443 [cs.CL]
(or arXiv:2606.06443v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.06443 Focus to learn more arXiv-issued DOI via DataCite
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