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arXiv · 2611.02172

Harassment on TikTok: A Mixed-Methods Analysis of Video Content and Psycholinguistic Comment Stances

Afsaneh Razi·Tyler Chang·Michal Monselise·Joseph J. Trybala·Asta Zelenkauskaite
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Abstract

Despite extensive research on harassment, how it manifests on short--form video platforms such as TikTok---and how it shapes user interactions---remains underexplored, though these dynamics are critical for designing effective safety strategies for online communities. We present a mixed-methods study of 190 TikTok videos tagged with hashtag harassment and their 66,337 comments. Our qualitative analysis of videos identifies harassment types (sexual, verbal, stalking, law enforcement, bullying, catcalling, and gaslighting) and their characteristics (e.g., location, target, video style, and theme), along with engagement patterns. Although sexual harassment was the most prevalent topic, it did not have the highest engagement; instead, stalking--related videos received the most views. Pairwise comparisons and regression--based analyses of comments' psycholinguistic features and stance (support, opposition, neutrality) revealed differences in emotional tone, social orientation, and cognitive categories across harassment types. Comments on sexual harassment frequently express solidarity with victims, whereas discussions of law enforcement harassment emphasize institutional critique. We provide implications of our findings, demonstrating that harassment on TikTok is multifaceted, shaped by content, context, and community norms.

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Afsaneh Razi, Tyler Chang, Michal Monselise, Joseph J. Trybala, Asta Zelenkauskaite. 2026-11-01. Harassment on TikTok: A Mixed-Methods Analysis of Video Content and Psycholinguistic Comment Stances. https://doi.org/10.1145/385940

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