发表机构
L3S Research Center, Leibniz Universität Hannover（莱布尼茨汉诺威大学L3S研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种无监督多阶段框架，整合主题建模、事件检测与链接，自动识别德国政客推文中的竞争性叙事，案例研究验证其有效性。
AI 中文摘要
社交媒体平台已成为塑造政治话语的核心场所，充当叙事形成和演变的舞台，并影响公众舆论。识别和分析这些叙事，特别是当它们在不同政治意识形态之间相互竞争时，对于理解现代政治传播的动态至关重要。本文提出了一种无监督框架，用于识别和描述社交媒体政治话语中的竞争性叙事，重点关注德国政治家的推文。该框架采用多阶段流水线，整合了主题建模、事件检测和事件链接等自然语言处理技术。通过将数据组织成连贯的故事并揭示用户社区的独特视角，该系统能够检测关键的竞争性叙事，突出围绕热门政治话题的分歧性框架和冲突。两个关于极化政治问题的案例研究证明了该方法的有效性，展示了其揭示和分析不同观点的能力。研究结果有助于更广泛地理解叙事如何在数字公共领域中传播，并为政策制定者、社交媒体平台以及对监测政治话语感兴趣的研究人员提供见解。
英文摘要
Social media platforms have become central to shaping political discourse, serving as arenas where narratives form and evolve, influencing public opinion. Identifying and analyzing these narratives, particularly when they compete across different political ideologies, is crucial for understanding the dynamics of modern political communication. This paper presents an unsupervised framework for identifying and characterizing competing narratives in political discourse on social media, focusing on German politicians' tweets. The framework employs a multi-stage pipeline that integrates natural language processing techniques such as topic modeling, event detection, and event linking. By forming data into coherent stories and uncovering the distinct perspectives of user communities, the system is able to detect the key competing narratives, highlighting the divergent framings and conflicts surrounding trending political topics. Two case studies on polarizing political issues demonstrate the efficacy of the methodology, showcasing its ability to uncover and analyze divergent viewpoints. The findings contribute to the broader understanding of how narratives propagate within the digital public sphere and offer insights for policymakers, social media platforms, and researchers interested in monitoring political discourse.
Comments11 pages, 5 figures. Published in the proceedings of Text2Story 2025, held with ECIR 2025
Journal refIn: Proceedings of Text2Story - Eighth Workshop on Narrative Extraction From Texts (Text2Story 2025), CEUR Workshop Proceedings, Vol. 3964, 2025, pp. 137-147
