SPILLOVER: Measuring Cyberbullying NormPropagation on Social Media

2026-07-21

AI Summary

This study investigates the propagation mechanism of cyberbullying behavior across sequential comments on social media, with a focus on the causal effect of prior comments on the aggressiveness of subsequent ones. Employing conversation fixed-effects models, textual similarity analysis, cross-conversation matching, and a fine-tuned HateBERT classifier, the authors systematically control for confounding factors such as lexical content and toxicity. The approach is validated across four datasets—Instagram, Reddit, Wikipedia, and SOCC—and reveals, for the first time, a significant inter-user bullying spillover effect: a preceding bullying comment substantially increases the likelihood that the next comment will also be bullying. Incorporating this binary signal markedly improves real-time prediction performance, offering a novel foundation for platform-level intervention strategies.

Abstract

While certain aspects of cyberbullying (CB) such as its factors and prevalence have been studied extensively, relatively little attention has been given to specifically how the aggression transfers from comment to comment. This understanding could have important implications for designing better anti-bullying features. In this paper, we study multiple aspects of the nature of this aggression transference in social media sessions. Using data from 32,754 consecutive comment pairs from 430 Instagram sessions, we find that a preceding CB comment substantially raises the odds of the next comment being CB, an effect confirmed by session fixed-effects controls and driven primarily by cross-user spread. We also find that $\text{CB} \to \text{CB}$ pairs are more textually similar than $\text{NoCB} \to \text{CB}$ pairs across five complementary methods, and that this pattern holds under a matched cross-session baseline that rules out shared vocabulary, session toxicity, and session length as confounds. Moreover, non-aggressive replies grow more negative as preceding CB severity increases, a graded pattern consistent with automatic emotional influence below the threshold of overt aggression. These key findings replicate across three independent datasets (Reddit, Wikipedia Detox, and SOCC), with spillover rates that track platform visibility design. Finally, we show that a single binary feature (whether the prior comment was CB) improves prediction over session-level baselines and over a fine-tuned HateBERT classifier, serving as a real-time moderation signal that targets the spreading chain rather than individual offenders.

Problem

• cyberbullying
• norm propagation
• aggression transference
• social media
• spillover effect

Innovation

• cyberbullying spillover
• norm propagation
• textual similarity
• real-time moderation
• cross-platform replication