Outlier Detection in Political Discourse: Unveiling Hidden Shifts and Manipulations on Social Media

While Rathje et al. (2024) and Yanovets & Smal (2020) focus on applying advanced models to analyze psychological and linguistic traits in political texts, they largely assume static or gradually shifting discourse. This idea goes further by developing a computational pipeline to detect deviations from expected linguistic patterns—for example, sudden surges in unusual sentiment, vocabulary, or framing in political tweets, campaign posts, or parliamentary transcripts. Building on techniques from fake news and cyberbullying detection (Kumar et al., 2024; Sowmya H.K. & Anandhi R.J., 2024), but shifting the focus from content classification to pattern deviation, this research would leverage unsupervised deep learning (e.g., autoencoders, transformers) to spot anomalies. Such an approach could expose coordinated inauthentic behavior, emergent protest movements, or shifts in propaganda before they become widely recognized—making it a valuable tool for political scientists, journalists, and platform moderators alike.

References:

1. GPT is an effective tool for multilingual psychological text analysis. Steve Rathje, Dan-Mircea Mirea, Ilia Sucholutsky, Raja Marjieh, Claire E. Robertson, J. V. Van Bavel (2024). Proceedings of the National Academy of Sciences of the United States of America.
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3. POLITICAL DISCOURSE CONTENT ANALYSIS: A CRITICAL OVERVIEW OF A COMPUTERIZED TEXT ANALYSIS PROGRAM LINGUISTIC INQUIRY AND WORD COUNT (LIWC). A. Yanovets, O. Smal (2020). Naukovì zapiski Nacìonalʹnogo unìversitetu «Ostrozʹka akademìâ». Serìâ «Fìlologìâ».
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