跨平台分析中量化政治党派倾向
Quantifying Political Partisanship for Cross-Platform Analyses
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中文总结 AI 辅助
研究社交媒体政治党派倾向测量问题，提出基于文本、以新闻可信度为锚定、可跨平台的方法，通过Transformer嵌入、聚类、标注及构建党派倾向轴打分，应用于特定语料库，实现跨平台比较，揭示相关党派动态。
中文摘要 AI 辅助
社交媒体上政治极化的研究依赖于可靠衡量用户生成内容中党派倾向的能力。现有方法通常针对特定平台属性定制，损害了跨平台的通用性。我们提出一种基于文本、可跨平台的方法来衡量社交媒体帖子中的政治党派倾向，以外部新闻可信度信号为锚定。利用基于Transformer的句子编码器嵌入帖子并聚类，用引用新闻媒体的聚合AllSides媒体偏见分数标注。在嵌入空间构建党派倾向轴，通过投影给单个帖子打分。我们将该方法应用于2024年美国总统大选前六个月从Bluesky和Truth Social收集的约130万篇帖子语料库，首次进行了这两个意识形态不对称平台上党派倾向分布的跨平台比较。结果表明，党派倾向分数与独立Twitter语料库中分布内和分布外的AllSides媒体偏见分数显著相关，并揭示了仅平台身份无法解释的平台内党派动态。
英文摘要
Research on political polarization on social media depends on the ability to reliably measure partisanship in user-generated content. However, existing approaches are typically tailored to platform-specific properties, such as structural affordances or linguistic conventions, which hurts generalizability across platforms. This limitation is increasingly consequential as the social media ecosystem fragments and fringe, alt-tech platforms emerge alongside mainstream ones. We propose a text-based, platform-portable methodology for measuring political partisanship in social media posts, anchored by an external news-credibility signal. Posts are embedded using a transformer-based sentence encoder and clustered into topic groups, which are labeled using the aggregated AllSides media bias scores of cited news outlets. A partisanship axis is then constructed in the embedding space as the difference between centroids of oppositely labeled clusters, and individual posts are scored by projection onto this axis. We apply the method to a corpus of approximately 1.3 million posts collected from Bluesky and Truth Social during the six months preceding the 2024 U.S. presidential election, providing the first cross-platform comparison of partisanship distributions on these two ideologically asymmetric platforms. The resulting partisanship scores correlate significantly with held-out AllSides media bias scores both in-distribution and out-of-distribution on an independent Twitter corpus, and recover within-platform partisan dynamics that platform identity alone cannot explain.
