Mapping The Emotional Landscape of Voter Behavior: A Transformer-Based Sentiment Analysis of Reddit Discourse in The 2024 U.S. Presidential Election

Berra Oğuz; Kerem Çolak; Zeynep Hilal Kilimci;

HIGHLIGHT

Beyond methodological contributions, the study integrates emotional indicators into the rational-actor assumptions of public choice theory, demonstrating that voter behavior is shaped by both rational and emotional factors.

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ABSTRACT

This study examines voter behavior in the context of the 2024 United States Presidential Election by analyzing user comments from Reddit. Owing to the platform’s anonymity and community diversity, Reddit provides signals that reflect a wide range of socio-demographic perspectives on the electoral process. A dataset of 7,788 comments was collected through the Reddit API and the PRAW library, then preprocessed (cleaning, normalization, stopword removal, and labeling) for sentiment analysis. In the experimental stage, five transformer-based models (BERT, RoBERTa, DeBERTa, ALBERT, and GPT-2) were comparatively evaluated. Results show that DeBERTa achieved the highest accuracy and F1 scores, while GPT-2 performed relatively poorly in classification tasks. Statistical analyses revealed that 51.6% of Trump-related comments were positive, whereas comments on Harris displayed a more fragmented distribution. Neutral sentiments dominated the “Unknown” category. These findings indicate that Reddit sentiment signals reflect not only descriptive observations but also statistically robust evidence for electoral forecasting. Beyond methodological contributions, the study integrates emotional indicators into the rational-actor assumptions of public choice theory, demonstrating that voter behavior is shaped by both rational and emotional factors. In doing so, it provides an interdisciplinary framework that validates the applicability of artificial intelligence methods to political behavior analysis.

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