Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions
Summary
This paper studies how LLM-based stance simulation in online discussions is sensitive to counterfactual revisions of conversational context, and proposes an auditing framework comparing text-only and multimodal strategies.
View Cached Full Text
Cached at: 06/05/26, 02:08 PM
Paper page - Revising Context, Shifting Simulated Stance: Auditing LLM-Based Stance Simulation in Online Discussions
Source: https://huggingface.co/papers/2606.06443
Abstract
LLM-based stance simulation exhibits context sensitivity when subjected to counterfactual revisions, with both text-only and multimodal approaches showing robust stance transitions across different polarization mechanisms.
Large language modelsare 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 inconversational contexts. In this work, we studycounterfactual context revisionas a framework for auditing LLM-basedstance 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 theconversational contextand 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 andstance transition rate. The results reveal effective and robust stance transitions in both text-only and multimodal strategies across differentpolarization-preference mechanisms. Our study contributes an evaluation framework for understanding the context sensitivity of LLM-basedstance simulation. More broadly, it highlights both the promise and risk of using LLMs to simulate online opinion dynamics.
View arXiv pageView PDFAdd to collection
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2606.06443 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2606.06443 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2606.06443 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
LLM-Assisted Stance Detection in Scientific Discourse: A Test Case in Bayesian Cognitive Science
This paper presents a method using LLMs for stance detection in scientific discourse, specifically identifying realism vs. instrumentalism in Bayesian cognitive science articles. The approach combines theory-driven coding, expert annotations, and prompt optimization to achieve high reliability.
Belief Engine: Configurable and Inspectable Stance Dynamics in Multi-Agent LLM Deliberation
The paper introduces the Belief Engine, an auditable belief-update layer for LLM agents that makes stance changes in multi-agent deliberation configurable and inspectable by treating belief as an evidential state with explicit update rules.
The Deliberative Illusion: Diagnosing Factual Attrition and Stance Homogenization in Multi-Agent LLM Deliberation
This paper identifies the 'deliberative illusion' in multi-agent LLM systems, where discussion causes factual attrition and stance homogenization, and introduces DelibTrace to measure these phenomena, showing that up to 72% of critical facts can be lost during deliberation.
Are you speaking my languages? On spoken language adherence in multimodal LLMs
This paper addresses the problem of spoken language adherence in multimodal LLMs for ASR, proposing a soft prompting approach and novel metric to quantify language violations. It evaluates three mitigation strategies—zero-shot prompting, supervised fine-tuning, and chain-of-thought reasoning—across multiple languages to improve transcription fidelity.
Towards Trust Calibration in Socially Interactive Agents: Investigating Gendered Multimodal Behaviors Generation with LLMs
This paper investigates the use of LLMs to generate multimodal behaviors (verbal, vocal, gestural, facial) for trust calibration in socially interactive agents. The study finds that while LLMs can produce coherent behaviors aligned with intended trustworthiness traits, they also reproduce societal gender stereotypes.