Style and Stance Alignment in Reddit Discourse

Abstract

This work investigates whether writing style is associated with stance alignment in large scale online discourse. We construct a Reddit based pipeline that transforms r/AskReddit questions and comments interactions into stance units using large language models (LLMs), stylistic embeddings, and semantic embeddings. The study begins with 169,053 original AskReddit questions, categorizes them into 48 semantic classes, and filters them for “stance suitability”. The full question corpus contains 176,932 rows; in the target stance evaluation stage, 22,238 original questions from 10 selected semantic classes are paired with up to 200 top level comments each and annotated by an LLM, yielding 277,474 comment level model outputs and 352,407 stance instances. Style is represented with answer text embeddings trained to capture writing style, while semantic context is represented with embeddings of question summaries, stance targets, and canonical opinions. We analyze 8,000,000 randomly sampled stance instance pairs by measuring style similarity, topic similarity, and stance label agreement. Across topic similarity thresholds from 0.50 to 0.95, pairs with high stylistic similarity consistently show higher model annotated stance agreement than the topic similar baseline, with lifts of approximately 6% to 12% points. A parallel augmentation analysis shows that synthetic questions produced by the tested models under generalize real class diversity, motivating the use of original questions for the main stance analysis. These findings suggest that writing style carries a weak but measurable signal related to stance alignment under semantically controlled conditions.