通过语义差异分析，揭示社交平台上政治话语长期极化的演变趋势。
When Words Divide: Diachronic Ideological Polarization in Political Discourse on Social Media
- 构建时间对齐的社区词向量，量化政治概念的语义分化。
- 发现政治极化在概念与话题层面均显著加剧，持续多年。
- 适用于研究社交媒体中意识形态演变，适合政策与社会学研究者。
政治极化已成为在线话语的核心特征，但其长期演变仍不清晰。本文通过对Reddit讨论的纵向分析，测量对立政治社群语言使用的语义差异。我们构建了时间对齐的社区特异性词嵌入，并将意识形态极化定义为政治概念随时间的语义偏离度。分析显示，在研究期间，意识形态极化在概念与话题层面均有显著上升。与以往主要聚焦于单一时点的情感极化或横断面分析不同，本方法捕捉了意识形态差异在语义框架上的动态演变。所提出的框架为大规模社交媒体话语中意识形态极化的时序动态研究提供了可扩展的方法。
原文摘要 · Abstract (English)
Political polarization has become a defining feature of online discourse, yet its long-term evolution remains poorly understood. We present a longitudinal analysis of ideological polarization in Reddit discussions by measuring semantic differences in the language used by opposing political communities. We construct temporally aligned community-specific word embeddings and quantify ideological polarization as the semantic divergence of political concepts over time. Our analysis shows that ideological polarization has increased substantially during the study period, both at the concept- and topic-level. Unlike prior computational work, which has largely focused on cross-sectional analyses or affective dimensions of polarization at a single point at time, our approach captures the evolution of ideological differences in semantic framing. The proposed framework provides a scalable method for studying the temporal dynamics of ideological polarization in large-scale social media discourse.
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