💬 NLP

Change My View? The Dynamics of Persuasion and Polarization in Online Discourse

By analyzing Reddit debates with large language models, this study demonstrates that belief revision in online discourse is driven more by relational strategies like concession and empathy than by direct logical refutation, suggesting that effective public reasoning requires balancing evidential content with empathetic framing.

Original authors: David Freeborn, Malihe Alikani, Anthony Sicilia

Published 2026-05-12

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: David Freeborn, Malihe Alikani, Anthony Sicilia

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Puzzle: Why Do We Argue More Instead of Agreeing?

Imagine you and a friend are watching a very intense courtroom drama on TV. You both see the exact same testimony, hear the exact same questions, and watch the same evidence. Logically, you should both come away with the same opinion about who is telling the truth.

But in real life, that rarely happens. Often, after seeing the same evidence, you and your friend end up more convinced of opposite sides than you were before. This is the "puzzle" the paper investigates: Why does sharing facts sometimes make us more polarized instead of bringing us together?

The Experiment: The "Change My View" Gym

To solve this, the researchers went to a specific corner of the internet called Reddit's r/ChangeMyView. Think of this forum as a giant, public gym for arguments.

The Goal: People post an opinion they hold, and others try to convince them to change their mind.
The Scoreboard: If someone is successfully convinced, they award a "delta" (a badge) to the person who changed their view. This is a rare, clear signal that persuasion actually worked.

The researchers gathered over 3,000 of these debates. They wanted to figure out: What specific things do people say that actually make the other person change their mind?

The Tool: The "Crystal Ball" Computer

The researchers used a powerful type of AI (Large Language Models) as a "crystal ball."

The Prediction: They showed the AI a debate that was only halfway finished and asked, "Based on what's been said so far, will the person change their mind by the end?"
The Reality Check: They then watched the rest of the debate to see if the AI was right.
The Discovery: The AI was surprisingly good at predicting the outcome. But the real magic happened when they asked the AI why it made that prediction.

The Secret Sauce: It's Not About the Facts (At First)

The researchers broke down every reply in the debates into ten different "moves" or strategies (like using logic, showing empathy, attacking the other person's character, or admitting you might be wrong).

Here is what they found, which might surprise you:

❌ The "Brute Force" Approach (Doesn't Work)
Imagine someone trying to break down a locked door by hitting it with a sledgehammer. In arguments, this is frontal refutation.

The Move: "You are wrong. Here are 10 facts. Your logic is bad."
The Result: This usually makes the other person dig their heels in. It's like hitting the door harder; the door just gets stuck. The paper found that pure logical attacks and attacking someone's credibility actually decreased the chances of winning the argument.

✅ The "Open Door" Approach (Works)
Imagine someone gently knocking on the door, saying, "Hey, I see why you're worried, and I agree with you on this one small thing. Can we look at this together?"

The Move: Concession (admitting the other person has a point) and Empathy (showing you understand their feelings).
The Result: This was the most successful strategy. By lowering the defenses first, the "door" opens, and the facts can actually get inside.

The Metaphor: The "Trust Filter"

Think of your brain as a security guard at a club.

The Facts are the VIPs trying to get in.
The Argument Style is the ID card.

If you walk up to the guard (the person you are arguing with) and shout, "I have the best ID! Look at my facts!" the guard (who is suspicious) will likely turn you away. The guard thinks, "This person is aggressive; they probably don't have good intentions."

However, if you walk up, smile, say, "I know you're having a tough day, and I actually agree with you about the music," the guard relaxes. The guard thinks, "Oh, this person is friendly; they might be telling the truth." Now, when you hand over your "Facts" (the VIPs), the guard lets them in.

The Big Takeaway

The paper concludes that persuasion is a relationship game, not just a math game.

Old Idea: If I give you enough good evidence, you must change your mind because you are rational.
New Finding: You can be rational and still reject evidence if you don't trust the person giving it. To change someone's mind, you first have to build a bridge of trust (empathy) and show you aren't trying to destroy them (concession).

Once that bridge is built, the logic and facts can finally cross over. Without the bridge, the facts just bounce off the wall.

In short: To win an argument, don't start by proving the other person wrong. Start by showing you understand them. That's the key to unlocking the door.

Technical Summary: The Dynamics of Persuasion and Polarization in Online Discourse

Problem Statement
The paper addresses the "puzzle of persuasion": the empirical observation that rational agents exposed to identical evidence often fail to converge on a shared view, and may instead become more polarized. This contradicts classical Bayesian epistemology (e.g., Blackwell & Dubins, 1962; Aumann, 1976), which suggests that agents with non-dogmatic priors observing the same data should eventually merge their opinions. While formal models (Freeborn, 2023, 2024) demonstrate how entangled priors can lead to rational factionalization, the specific rhetorical mechanisms that facilitate or hinder belief revision in natural language discourse remain under-explored. The study seeks to determine which argumentative strategies effectively induce belief change in a real-world setting and how these strategies interact with probabilistic forecasts of persuasion.

Methodology
The authors employ a novel hybrid methodology combining large-scale computational analysis with philosophical conceptualization, utilizing the Reddit r/ChangeMyView (CMV) corpus as a testbed.

Data Source: The study analyzes 3,051 original CMV posts and approximately 293,000 comments. Success is operationally defined by the awarding of a "delta" badge by the Original Poster (OP), signaling a publicly acknowledged change of view.
Forecasting Baseline: Following Sicilia et al. (2024), the authors use Large Language Models (LLMs), specifically Llama-3 variants (8B and 70B), to forecast the probability of a delta being awarded. The models are prompted with partial conversations (truncated at a random intermediate turn) and asked to estimate the likelihood of persuasion on a 1–10 scale, which is mapped to a probability $p$ .
Strategy Coding Pipeline: A two-stage LLM pipeline annotates every reply for rhetorical strategies:
- Free Elicitation: A model generates verb-phrase descriptions of strategies used in a reply.
- Taxonomy Induction: A second model clusters these phrases into ten super-categories (e.g., "Conceding and compromising," "Building empathy," "Logical reasoning").
Statistical Modeling: The authors train logistic regression models to predict persuasion outcomes. They compare three model specifications:
- Strategy-Only (S-only): Uses only the binary presence of rhetorical strategies.
- Additive (LLM + S): Combines the baseline LLM probability with strategy features.
- Interaction (LLM × S): Models the interaction between the baseline probability and specific strategies to test if strategy efficacy is conditional on the conversational context.

Key Results
The study yields three primary empirical findings:

Forecasting Calibration: Uncertainty-aware Chain-of-Thought (CoT) prompting significantly improves the calibration of LLMs. For instance, the Llama 3.1 70B model improved its Brier score from 0.31 to 0.24 and F1 score from 0.56 to 0.78 when tuned for uncertainty, demonstrating that LLMs can serve as reliable "ideal observers" for conversational outcomes.
Predictive Power of Strategies: The inclusion of rhetorical strategy features markedly improves predictive power over baseline models. The Interaction Model consistently outperforms additive models, indicating that the efficacy of a strategy is not merely additive but depends on the existing conversational context (the baseline probability).
Differential Impact of Rhetorical Moves:
- Positive Predictors: Strategies involving concession and compromise (coefficient +0.86, $p < 0.001$ ) and building empathy/rapport (coefficient +0.39, $p < 0.001$ ) are the strongest positive predictors of belief change.
- Negative Predictors: Strategies involving challenging assumptions/arguments (coefficient -0.37, $p < 0.001$ ), deflecting attention (coefficient -0.48, $p < 0.001$ ), and even logical reasoning and evidence (coefficient -0.16, $p = 0.029$ ) are associated with a lower probability of persuasion.
- Insignificant Factors: Establishing credibility/authority and emotional appeals showed no statistically significant effect in the interaction model.

Significance and Claims
The paper claims to offer a "micro-level view" of when evidence overcomes prior commitments, bridging the gap between dialectical (truth-oriented) and rhetorical (influence-oriented) traditions of persuasion.

Relational Framing over Evidential Content: The findings suggest that effective public reasoning depends as much on relational framing (concession, empathy) as on evidential content. Direct refutation and "evidential bombardment" often fail because they activate background belief nodes where agents diverge, leading to rational entrenchment.
Mechanism of Belief Revision: The authors interpret these results through Freeborn's (2023, 2024) Bayesian network model. Concession and empathy are not irrational substitutes for reason but are "conversational tactics" that modulate higher-order channels. By enlarging the shared "context set" (Stalnaker, 2002) and reducing epistemic risk, these moves allow subsequent factual claims to be processed through overlapping internal pathways, facilitating convergence.
Refinement of Normative Accounts: The study invites a refinement of normative accounts of rational dialogue, suggesting that concessionary dialogue may be rationally mandated as a precursor to assessing total evidence, rather than a sign of weakness.

Limitations and Scope
The authors maintain a modest stance regarding the generalizability of their findings. They acknowledge that the CMV corpus represents a self-selected population of English speakers open to debate, potentially underrepresenting polarizing political topics. Furthermore, the operational definition of persuasion (the delta badge) is coarse, capturing only explicit belief changes and likely missing partial or private shifts. The use of LLMs for both prediction and annotation introduces a risk of circularity, though the study employs cross-validation and calibration metrics to mitigate this. The paper concludes that while these tools illuminate the rhetorical scaffolding of belief change, they do not yet fully explain the complex dynamics of polarization in broader, less cooperative networks.