Change My View? The Dynamics of Persuasion and Polarization in Online Discourse
- URL: http://arxiv.org/abs/2605.08383v1
- Date: Fri, 08 May 2026 18:40:51 GMT
- Title: Change My View? The Dynamics of Persuasion and Polarization in Online Discourse
- Abstract summary: We use large language models to analyze a corpus of debates on Reddit's r/ChangeMyView.<n>We find that effective public reasoning depends as much on relational framing as on evidential content.
- Score: 6.390103958990699
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Philosophical accounts of persuasion often assume that shared evidence and rational argumentation should lead to a convergence of views between peers, yet everyday discourse often suggests otherwise. In this study, we use large language models to analyze a corpus of debates on Reddit's r/ChangeMyView, where belief revision is publicly signaled. Large language models were asked, halfway through each discussion, to forecast whether such an acknowledgement would arise; their probabilistic estimates serve as a conversational baseline. Each reply was then coded, through a hybrid machine-assisted procedure, for ten familiar rhetorical strategies -- concession, empathy, logical challenge, credibility appeals, and so forth. Adding these strategic features markedly improves predictive power and yields a consistent pattern: moves that express concession or empathetic alignment substantially increase the prospect of belief change, whereas frontal refutation, credibility attacks, and topic deflection diminish it. The findings indicate that effective public reasoning depends as much on relational framing as on evidential content, and they invite a refinement of normative accounts of rational dialogue.
Related papers
- ARGUS: Theory-of-Mind Guided Argument Generation with Strategy-Aware Planning and Knowledge Grounding [5.0921317897723215]
We propose Argus, an agent-based framework that operationalizes classical rhetoric for persuasive writing.<n>At its core, a Theory-of-Mind (ToM) Reasoner constructs an explicit dual mental model of the audience's beliefs and values to guide downstream decisions.<n>We evaluate Argus across three diverse benchmarks using both automated pairwise Elo and LLM-as-judge metrics.
arXiv Detail & Related papers (2026-08-06T02:26:10Z) - Look on Demand: A Cognitive Scheduling Framework for Visual Evidence Acquisition in Multimodal Reasoning [82.86343313807158]
We argue that a central challenge is how and when visual evidence is introduced into the reasoning process.<n>Motivated by this insight, we propose CSMR, a multimodal reasoning framework in which a language model controls the reasoning process.
arXiv Detail & Related papers (2026-05-27T08:43:13Z) - The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning [76.96796481909581]
Internal cognitive processing is instrumental in formulating high-quality responses.<n>We propose a novel method named FLAIR that conducts latent thinking simultaneously with speech perception.<n>Our approach aligns seamlessly with spoken dialogue systems.
arXiv Detail & Related papers (2026-03-18T15:30:29Z) - Persuasion in Online Conversations Is Associated with Alignment in Expressed Human Values [3.52359746858894]
We investigate how the expression and alignment of human values in back-and-forth online discussions relate to persuasion.<n>Using data from Reddit's ChangeMyView subreddit, we analyze one-on-one exchanges and characterize participants' value expression.
arXiv Detail & Related papers (2026-01-19T03:08:25Z) - Analyzing Reasoning Consistency in Large Multimodal Models under Cross-Modal Conflicts [74.47786985522762]
We identify a critical failure mode termed textual inertia, where models tend to blindly adhere to the erroneous text while neglecting conflicting visual evidence.<n>We propose the LogicGraph Perturbation Protocol that structurally injects perturbations into the reasoning chains of diverse LMMs.<n>Results reveal that models successfully self-correct in less than 10% of cases and predominantly succumb to blind textual error propagation.
arXiv Detail & Related papers (2026-01-07T16:39:34Z) - MMPersuade: A Dataset and Evaluation Framework for Multimodal Persuasion [73.99171322670772]
Large Vision-Language Models (LVLMs) are increasingly deployed in domains such as shopping, health, and news.<n> MMPersuade provides a unified framework for systematically studying multimodal persuasion dynamics in LVLMs.
arXiv Detail & Related papers (2025-10-26T17:39:21Z) - Disagreements in Reasoning: How a Model's Thinking Process Dictates Persuasion in Multi-Agent Systems [49.69773210844221]
This paper challenges the prevailing hypothesis that persuasive efficacy is primarily a function of model scale.<n>Through a series of multi-agent persuasion experiments, we uncover a fundamental trade-off we term the Persuasion Duality.<n>Our findings reveal that the reasoning process in LRMs exhibits significantly greater resistance to persuasion, maintaining their initial beliefs more robustly.
arXiv Detail & Related papers (2025-09-25T12:03:10Z) - Echoes of Agreement: Argument Driven Opinion Shifts in Large Language Models [0.36713387874278247]
We conduct experiments for political bias evaluation in presence of supporting and refuting arguments.<n>Our experiments show that such arguments substantially alter model responses towards the direction of the provided argument.<n>These effects point to a sycophantic tendency in LLMs adapting their stance to align with the presented arguments.
arXiv Detail & Related papers (2025-08-11T20:54:14Z) - Persua: A Visual Interactive System to Enhance the Persuasiveness of
Arguments in Online Discussion [52.49981085431061]
Enhancing people's ability to write persuasive arguments could contribute to the effectiveness and civility in online communication.
We derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions.
Persua is an interactive visual system that provides example-based guidance on persuasive strategies to enhance the persuasiveness of arguments.
arXiv Detail & Related papers (2022-04-16T08:07:53Z) - A pragmatic account of the weak evidence effect [7.277760003553327]
We show that weak evidence should only backfire when speakers are expected to act under persuasive goals.
We introduce a simple experimental paradigm called the Stick Contest to measure the extent to which the weak evidence effect depends on speaker expectations.
Our findings suggest potential avenues for rational models of social reasoning to further illuminate decision-making phenomena.
arXiv Detail & Related papers (2021-12-07T16:16:01Z) - Strategic Argumentation Dialogues for Persuasion: Framework and
Experiments Based on Modelling the Beliefs and Concerns of the Persuadee [6.091096843566857]
Two key dimensions for determining whether an argument is good in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience.
We present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues.
arXiv Detail & Related papers (2021-01-28T08:49:24Z) - What Changed Your Mind: The Roles of Dynamic Topics and Discourse in
Argumentation Process [78.4766663287415]
This paper presents a study that automatically analyzes the key factors in argument persuasiveness.
We propose a novel neural model that is able to track the changes of latent topics and discourse in argumentative conversations.
arXiv Detail & Related papers (2020-02-10T04:27:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.