Network Analysis of Cyberbullying Interactions on Instagram

TL;DR

This paper applies network science to analyze cyberbullying interactions on Instagram, revealing common patterns, roles, and the prevalence of negative interactions, and introduces new heuristics and metrics for understanding these dynamics.

Contribution

It introduces a novel network analysis framework with heuristics, Bully and Victim Scores, and motif analysis to study cyberbullying interactions on Instagram at a granular level.

Findings

Most sessions have negative Victim Scores indicating more attacks than support.

Bully roles are most common in motifs, but defenders are also frequently present.

Cyberbullying interactions often feature recurring structural patterns.

Abstract

Cyberbullying continues to grow in prevalence and its impact is felt by thousands worldwide. This study seeks a network science perspective on cyberbullying interaction patterns on the popular photo and video-sharing platform, Instagram. Using an annotated cyberbullying dataset containing over 400 Instagram posts, we outline a set of heuristics for building Session Graphs, where nodes represent users and their cyberbullying role, and edges represent their exchanged communications via comments. Over these graphs, we compute the Bully Score, a measure of the net malice introduced by bullies as they attack victims (attacks minus pushback), and the Victim Score, a measure of the net support victims receive from their defenders (support minus attacks). Utilizing small subgraph (motif) enumeration, our analysis uncovers the most common interaction patterns over all cyberbullying sessions. We also explore the prevalence of specific motif patterns across different ranges of Bully and Victim Scores. We find that a majority of cyberbullying sessions have negative Victim Scores (attacks outweighing support), while the Bully Score distribution has a slight positive skew (attacks outweighing pushback). We also observe that while bullies are the most common role in motifs, defenders are also consistently present. This suggests that bullying mitigation is a recurring structural feature of many interactions. To the best of our knowledge, this is the first study to explore this granular scale of network interactions using human annotations at the session and comment levels on Instagram.