The language of opinion change on social media under the lens of communicative action
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Abstract (arXiv)
Which messages are more effective at inducing a change of opinion in the listener? We approach this question within the frame of Habermas' theory of communicative action, which posits that the illocutionary intent of the message (its pragmatic meaning) is the key. Thanks to recent advances in natural language processing, we are able to operationalize this theory by extracting the latent social dimensions of a message, namely archetypes of social intent of language, that come from social exchange theory. We identify key ingredients to opinion change by looking at more than 46k posts and more than 3.5M comments on Reddit's r/ChangeMyView, a debate forum where people try to change each other's opinion and explicitly mark opinion-changing comments with a special flag called "delta". Comments that express no intent are about 77% less likely to change the mind of the recipient, compared to comments that convey at least one social dimension. Among the various social dimensions, the ones that are most likely to produce an opinion change are knowledge, similarity, and trust, which resonates with Habermas' theory of communicative action. We also find other new important dimensions, such as appeals to power or empathetic expressions of support. Finally, in line with theories of constructive conflict, yet contrary to the popular characterization of conflict as the bane of modern social media, our findings show that voicing conflict in the context of a structured public debate can promote integration, especially when it is used to counter another conflictive stance. By leveraging recent advances in natural language processing, our work provides an empirical framework for Habermas' theory, finds concrete examples of its effects in the wild, and suggests its possible extension with a more faceted understanding of intent interpreted as social dimensions of language.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Oct 2022.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 4; 2026: 5.
12 papers.
- 2023The Persuasive Power of Large Language Modelscited by 47
- 2024Y Social: an LLM-powered Social Media Digital Twincited by 44
- 2025Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statisticscited by 8
- 2025Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Redditcited by 4
- 2026"I understand your perspective": LLM Persuasion through the Lens of Communicative Action Theorycited by 4
- 2023Dimensions of Online Conflict: Towards Modeling Agonismcited by 2
- 2025Disentangling Interaction and Bias Effects in Opinion Dynamics of Large Language Modelscited by 2
- 2025A Similarity Measure for Comparing Conversational Dynamicscited by 1
- 2026P1SCO: Social Dimensions from a Perspectivist Lenscited by 1
- 2026Collective Opinion Dynamics in Structured LLM Populationscited by 0
- 2026Learning Perspectivist Social Meaning via Demographic-Conditioned Fusion Embeddingscited by 0
- 2026Persuasion Index: A Theory-Guided Framework for Persuasion Analysiscited by 0
- 2026Persuasion Index: A Theory-Guided Framework for Persuasion Analysis12 Jun 2026
- 2026"I understand your perspective": LLM Persuasion through the Lens of Communicative Action Theory6 Jun 2026
- 2026Learning Perspectivist Social Meaning via Demographic-Conditioned Fusion Embeddings5 Jun 2026
- 2026P1SCO: Social Dimensions from a Perspectivist Lens25 May 2026
- 2026Collective Opinion Dynamics in Structured LLM Populations13 Apr 2026
- 2025Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit8 Oct 2025
- 2025Disentangling Interaction and Bias Effects in Opinion Dynamics of Large Language Models8 Sep 2025
- 2025A Similarity Measure for Comparing Conversational Dynamics25 Jul 2025
- 2025Tailored Truths: Optimizing LLM Persuasion with Personalization and Fabricated Statistics28 Jan 2025
- 2024Y Social: an LLM-powered Social Media Digital Twin1 Aug 2024
- 2023The Persuasive Power of Large Language Models24 Dec 2023
- 2023Dimensions of Online Conflict: Towards Modeling Agonism6 Nov 2023