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BERTopic-VP is a virality-prioritised topic-modelling framework that combines clustering with a misinformation detection module to analyze health misinformation on Twitter, achieving high classification performance for COVID-19 and Monkeypox outbreaks.
This paper systematically compares the impact of model size on topic quality using seven transformer-based language models in a BERTopic pipeline, finding that model size has negligible effect on topic coherence, suggesting smaller models can perform comparably to larger ones.
This paper compares Structural Topic Models (STM) and BERTopic for analyzing short, open-ended survey responses, finding that BERTopic with contextual augmentation yields better topic coherence and interpretability, while STM offers stronger support for inferential covariate analysis.