用智能体与图网络量化社交群体极化，更准更可解释。
A More Advanced Group Polarization Measurement Approach Based on LLM-Based Agents and Graphs
- 构建多智能体系统+情感图谱，融合文本与关系信息。
- 提出社区对立指数（COI），实现极化程度量化评估。
- 零样本立场识别表现优异，适合社会舆情分析场景。
群体极化是社交媒体内容分析的重要方向，但现有方法难以有效测量。主要挑战包括：海量文本处理困难、语义复杂（如讽刺、梗、网络用语）以及文本碎片化导致整体性分析难。为此，本文设计基于多智能体系统的解决方案，采用图结构的社区情感网络（CSN）表征极化状态，并提出社区对立指数（COI）进行量化。通过零样本立场检测任务验证，该方法在准确性与可解释性方面表现突出，具有显著实用价值。
原文摘要 · Abstract (English)
Group polarization is an important research direction in social media content analysis, attracting many researchers to explore this field. Therefore, how to effectively measure group polarization has become a critical topic. Measuring group polarization on social media presents several challenges that have not yet been addressed by existing solutions. First, social media group polarization measurement involves processing vast amounts of text, which poses a significant challenge for information extraction. Second, social media texts often contain hard-to-understand content, including sarcasm, memes, and internet slang. Additionally, group polarization research focuses on holistic analysis, while texts is typically fragmented. To address these challenges, we designed a solution based on a multi-agent system and used a graph-structured Community Sentiment Network (CSN) to represent polarization states. Furthermore, we developed a metric called Community Opposition Index (COI) based on the CSN to quantify polarization. Finally, we tested our multi-agent system through a zero-shot stance detection task and achieved outstanding results. In summary, the proposed approach has significant value in terms of usability, accuracy, and interpretability.
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