SOURCE-LINKED INTELLIGENCE
Detecting Authorship in Political Texts with Inductive Stylometry
Political texts are rarely authored by the nominal speaker alone. Tweets, speeches, reports, and official statements are drafted, edited, or harmonized by staff, yet political science has paid limited attention to the stylistic traces these hidden authors leave behind. This paper develops and stress-tests an inductive stylometric approach for recovering latent authorship structure in political communication, combining character 3-gram features with UMAP dimensionality reduction, and Burrows' Delta. We apply the approach to six corpora that vary in length (from tweets to long documents), in mod
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-08T08:57:17.000Z
First collected: 2026-09-20T20:22:01.598Z. This is not the publication date.