[Paper] From Moderation to Mediation: Can LLMs Serve as Mediators in Online Flame Wars?

Source: arXiv - 2512.03005v1

Overview

The paper investigates a bold new role for large language models (LLMs): acting as mediators in heated online discussions, or “flame wars,” rather than just flagging toxic content. By breaking mediation down into judgment (assessing fairness and emotion) and steering (crafting empathetic, de‑escalating replies), the authors explore whether current LLMs can help steer conversations toward constructive outcomes.

Key Contributions

• Mediation Framework: Introduces a two‑step pipeline—judgment and steering—that equips LLMs to both evaluate conflict dynamics and generate calming interventions.
• Reddit‑Based Mediation Dataset: Curates a large, annotated collection of real‑world flame‑war threads, complete with fairness scores, emotional tags, and ground‑truth mediation responses.
• Multi‑Stage Evaluation Protocol: Combines principle‑based scoring (fairness, empathy, relevance), simulated user interaction, and human expert comparison to assess mediation quality.
• Empirical Benchmark: Shows that commercial API models (e.g., GPT‑4, Claude) outperform open‑source LLMs on both judgment accuracy and steering alignment.
• Insightful Failure Analysis: Identifies systematic blind spots (e.g., cultural nuance, long‑term persuasion) that limit current models’ mediation effectiveness.

Methodology

1. Data Collection

   • Scraped thousands of Reddit comment threads flagged as “heated” or “toxic.”
   • Human annotators labeled each turn for fairness (who’s right/wrong), emotional intensity, and provided a gold‑standard mediator reply.

2. Model Design

   • Judgment Module: Prompt‑engineered LLM predicts fairness scores and emotional states for each participant.
   • Steering Module: Takes the judgment output and generates a single, empathetic response aimed at de‑escalation (e.g., re‑framing, asking clarifying questions).

3. Evaluation Pipeline

   • Principle‑Based Scoring: Automated metrics check if the response respects fairness, empathy, and relevance guidelines.
   • User Simulation: A secondary LLM plays the role of a participant, replying to the mediator’s message; the conversation’s toxicity trajectory is tracked.
   • Human Comparison: Domain experts rate the mediator’s output against human‑crafted baselines on clarity, helpfulness, and conflict resolution.

Results & Findings

• API models consistently produce more nuanced fairness assessments and generate responses that users (simulated or real) perceive as genuinely empathetic.
• Open‑source models lag behind, often missing subtle emotional cues or offering generic, sometimes patronizing, advice.
• In simulated dialogues, mediator‑augmented threads showed a 30 % reduction in toxicity scores compared with unmediated baselines.
• Human judges preferred LLM‑generated mediations over baseline moderation tools in 68 % of cases.

Practical Implications

• Platform Moderation Suites: Integrating a mediation layer could turn “delete‑or‑warn” pipelines into proactive conversation‑repair tools, reducing user churn and improving community health.
• Customer Support & Community Management: Companies can deploy LLM mediators to defuse angry tickets or forum disputes before escalation, saving time and preserving brand reputation.
• Developer Toolkits: The two‑step API (judgment + steering) can be wrapped into SDKs, allowing developers to plug mediation into chatbots, gaming chat, or collaborative workspaces with minimal prompt engineering.
• Policy & Compliance: Empathetic mediation aligns with emerging regulations that demand not just content removal but harm reduction and user well‑being.

Limitations & Future Work

• Cultural & Contextual Gaps: Current models struggle with nuanced cultural references and may misinterpret sarcasm, leading to inappropriate interventions.
• Long‑Term Persuasion: The study focuses on single‑turn interventions; sustained conflict resolution over multiple exchanges remains an open challenge.
• Open‑Source Gap: Performance disparity highlights the need for more accessible, high‑quality open‑source LLMs or fine‑tuning recipes tailored to mediation.
• Evaluation Fidelity: Simulated user models may not capture real‑world emotional reactions; larger‑scale live A/B tests are required to validate real impact.

Bottom line: While still early, the research shows that LLMs can move beyond policing language to actively guiding healthier online discourse—a promising step toward AI‑augmented social mediation.

Authors

• Dawei Li
• Abdullah Alnaibari
• Arslan Bisharat
• Manny Sandoval
• Deborah Hall
• Yasin Silva
• Huan Liu

Paper Information

• arXiv ID: 2512.03005v1
• Categories: cs.AI
• Published: December 2, 2025
• PDF: Download PDF