用图模型梳理海量观点，帮读者看清复杂争议中的立场分布。
A Community-Based Approach for Stance Distribution and Argument Organization
- 构建多关系图谱，捕捉论点间语义、主题、关键词等关联
- 无监督识别观点社区，揭示同质与异质立场分布模式
- 适合研究社会政治议题的分析者，无需标注数据
在线辩论平台和社交媒体催生了大量关于争议话题的多视角论点内容。尽管这些多元观点有助于培养批判性思维、打破信息茧房（Pariser, 2011），但其数量庞大且结构复杂，使读者难以有效整合与理解。本文提出一种无监督的基于图的方法，用于社区化组织论点，帮助用户导航复杂论证格局。系统分析聚焦特定主题的文章集合，通过捕捉论点间的多种关系类型——主题相似性、语义连贯性、共享关键词与共同实体——构建丰富的交互图谱。随后利用社区检测算法识别出反映同质与异质观点分布的论点社区。通过有策略的图操作简化社区结构，向用户提供可读性强又全面的论点模式摘要。该方法无需训练数据，可高效处理数百篇文章，并保留论点间细微关系。实验表明，系统能有效识别有意义的论点社区，并以可解释方式呈现，显著提升用户对复杂社会政治议题的理解能力。
原文摘要 · Abstract (English)
The proliferation of online debate platforms and social media has led to an unprecedented volume of argumentative content on controversial topics from multiple perspectives. While this wealth of perspectives offers opportunities for developing critical thinking and breaking filter bubbles (Pariser 2011), the sheer volume and complexity of arguments make it challenging for readers to synthesize and comprehend diverse viewpoints effectively. We present an unsupervised graph-based approach for community-based argument organization that helps users navigate and understand complex argumentative landscapes. Our system analyzes collections of topic-focused articles and constructs a rich interaction graph by capturing multiple relationship types between arguments: topic similarity, semantic coherence, shared keywords, and common entities. We then employ community detection to identify argument communities that reveal homogeneous and heterogeneous viewpoint distributions. The detected communities are simplified through strategic graph operations to present users with digestible, yet comprehensive summaries of key argumentative patterns. Our approach requires no training data and can effectively process hundreds of articles while preserving nuanced relationships between arguments. Experimental results demonstrate our system's ability to identify meaningful argument communities and present them in an interpretable manner, facilitating users' understanding of complex socio-political debates.
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