Large-Scale Hate Speech Analysis with BERT and HDBSCAN: Tracking Political Influence Across 55M Reddit Posts

The interplay between political campaigning and online discourse has emerged as a critical area of computational social science, particularly concerning the propagation of hate speech. This study investigates how Donald Trump’s 2024–2025 U.S. presidential campaign influenced the prevalence and thematic focus of hate speech on Reddit, a platform known for its politically engaged communities. Leveraging a dataset of over 55 million Reddit posts spanning two key periods (pre- and mid-campaign), we develop a large-scale Natural Language Processing (NLP) methodology to detect and analyse the dynamics of hate speech. Hate speech is identified using a fine-tuned BERT model optimized for multi-class classification, distinguishing hateful, offensive, and neutral content. To explore the structure of hate speech targets, we apply Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), a density-based clustering algorithm that accommodates variable data distributions in high-dimensional embeddings. A post-processing algorithm further refines overlapping categories by decomposing identity-based slurs into distinct subgroups. Our findings reveal a marked increase in hate speech volume during the campaign, along with significant shifts in targeted identity clusters, particularly toward ideological, gendered, and racial groups. These results contribute empirical evidence to debates around algorithmic content moderation, political communication, and the computational modeling of social polarization.