Summary
Cyberbullying isn't just about individual abusive messages; it involves patterns of behavior and social connections. The authors developed a new method that uses a detailed, structured way of describing cyberbullying behavior in Bengali online spaces. By analyzing online users with multiple bullying comments, their method groups them into communities based on their behavior and context, helping to understand how cyberbullying spreads and who the key players are. This approach helps explain how users influence each other and form groups, offering a clearer picture of online harassment patterns.
What this means in practice
- •For social media platform moderators: Identify persistent perpetrators and behavioral communities in Bengali social networks to support targeted content moderation and intervention strategies.
- •For online community managers: Use behavioral similarity networks to understand user influence patterns and improve community health in Bengali language forums or social sites.
- •For content recommendation engineers: Enhance recommendation algorithms by incorporating community-based behavioral features to mitigate exposure to bullying content in regional language systems.$Commercial implications: Enables development of safer content recommendation systems for Bengali platforms by detecting and avoiding promotion of bullying behavior.
Tested on one dataset.
Authors
Md. Nesarul Hoque, Md Hanif Seddiqui, Miskatul Anwara, Rudra Pratap Deb Nath
Abstract
Cyberbullying involves behavioral and contextual patterns beyond isolated abusive messages. However, existing approaches remain fragmented across content detection, semantic representation, and network analysis, with limited interpretable perpetrator representations, multi-scale structural modeling, and explanations of behavior-driven similarity and influence propagation. These limitations hinder the identification of persistent perpetrators, characterization of behavioral communities, and assessment of potentially influential users. This paper proposes Ontology-Driven Cyberbullying Behavior and Network Analysis (ODCBNA), a perpetrator-centric framework integrating requirements-driven ontology design, knowledge graph construction, behavioral modeling, community analysis, and influence modeling. Using the Bengali Contextual and Diverse Cyberbullying (BenCoDiC) corpus as a use case, ODCBNA identifies $1{,}063$ users with at least three bullying comments or replies as perpetrators and represents each through a $64$-dimensional behavioral and contextual feature vector. Cosine similarity and $k$-nearest-neighbor modeling produce a weighted network with $27{,}554$ edges. Louvain analysis identifies six communities with a modularity of approximately $0.495$ and $27$ sub-communities. Feature-contribution analysis identifies the Video, Impact, and User Profile groups as the strongest contributors to behavioral similarity and community separation, while contextual dimensions support interpretation. Community-constrained influence analysis shows that IM Greedy achieves small positive gains in all six communities, although different seed-selection methods produce comparable diffusion spreads despite limited seed overlap. These findings provide an interpretable basis for understanding perpetrator behavior, community organization, and potential influence patterns in Bengali online environments.