Old but Gold: LLM-Based Features and Shallow Learning Methods for Fine-Grained Controversy Analysis in YouTube Comments

Online discussions can either bridge differences through constructive dialogue or amplify divisions through destructive interactions.This paper proposes a computational approach to analyze dialogical relation patterns in YouTube comments, offering a fine-grained framework for controversy detection, enabling also analysis of individual contributions.Our experiments demonstrate that shallow learning methods, when equipped with theoreticallygrounded features, consistently outperform more complex language models in characterizing discourse quality at both comment-pair and conversation-chain levels.Ablation studies confirm that divisive rhetorical techniques serve as strong predictors of destructive communication patterns.This work advances understanding of how communicative choices shape online discourse, moving beyond engagement metrics toward nuanced examination of constructive versus destructive dialogue patterns.