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.

翻译：群体极化是社交媒体内容分析的重要研究方向，吸引了众多研究者探索该领域。因此，如何有效测量群体极化已成为关键课题。在社交媒体上测量群体极化面临若干现有解决方案尚未解决的挑战。首先，社交媒体群体极化测量涉及处理海量文本，这对信息提取构成了重大挑战。其次，社交媒体文本常包含难以理解的内容，包括讽刺、网络迷因和网络俚语。此外，群体极化研究侧重于整体分析，而文本通常是碎片化的。为应对这些挑战，我们设计了一种基于多智能体系统的解决方案，并使用图结构的社区情感网络来表示极化状态。进一步地，我们基于CSN开发了一种称为社区对立指数的度量指标来量化极化程度。最后，我们通过零样本立场检测任务测试了多智能体系统，并取得了优异的结果。综上所述，所提出的方法在可用性、准确性和可解释性方面具有重要价值。