用大模型和规则分析社交媒体情绪极化，发现事件前后两种不同极化模式。
Measuring Social Media Polarization Using Large Language Models and Heuristic Rules
- 结合大模型与领域规则，自动提取立场、情绪和互动模式。
- 发现极化分预热型（事件前升级）和反应型（事件后飙升）两种模式。
- 方法可处理单条互动，适合研究突发公共事件下的舆论变化。
理解在线话语中的情绪极化对评估社交媒体的社会影响至关重要。本研究提出一种新框架，利用大语言模型（LLMs）和领域知识启发的规则，系统分析气候变迁、枪支管控等争议话题中的情绪极化。不同于以往依赖情感分析或预设分类器的方法，该方法通过大模型提取立场、情绪基调及共识模式，并设计基于规则的评分体系，即使在仅含单次互动的小规模对话中也能量化情绪极化，依据立场一致性、情绪内容与互动动态。分析揭示两类显著极化模式：(i) 预热驱动型极化，即重大事件前极端极化上升；(ii) 反应型极化，即突发事件后情绪极化迅速飙升。结合AI内容标注与领域规则，该框架具备可扩展性与可解释性。源代码已公开：https://github.com/hasanjawad001/llm-social-media-polarization。
原文摘要 · Abstract (English)
Understanding affective polarization in online discourse is crucial for evaluating the societal impact of social media interactions. This study presents a novel framework that leverages large language models (LLMs) and domain-informed heuristics to systematically analyze and quantify affective polarization in discussions on divisive topics such as climate change and gun control. Unlike most prior approaches that relied on sentiment analysis or predefined classifiers, our method integrates LLMs to extract stance, affective tone, and agreement patterns from large-scale social media discussions. We then apply a rule-based scoring system capable of quantifying affective polarization even in small conversations consisting of single interactions, based on stance alignment, emotional content, and interaction dynamics. Our analysis reveals distinct polarization patterns that are event dependent: (i) anticipation-driven polarization, where extreme polarization escalates before well-publicized events, and (ii) reactive polarization, where intense affective polarization spikes immediately after sudden, high-impact events. By combining AI-driven content annotation with domain-informed scoring, our framework offers a scalable and interpretable approach to measuring affective polarization. The source code is publicly available at: https://github.com/hasanjawad001/llm-social-media-polarization.
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