arXiv cs.CL· Md. Nesarul Hoque, Md Hanif Seddiqui, Miskatul Anwara, Rudra Pratap Deb Nath·· 13 小时前AI 评分20
ODCBNA：面向孟加拉语网络欺凌分析的本体驱动框架
From Perpetrators' Digital Footprints to Behavioral Communities: An Ontology-Driven Framework for Bengali Cyberbullying Analysis--- FULL VERSION
AI 导读
研究提出以施害者为中心的 ODCBNA 框架，整合本体设计、知识图谱构建、行为建模、社区分析与影响力建模。基于 BenCoDiC 语料，识别出 1,063 名至少有 3 条欺凌评论或回复的施害者，用 64 维行为与上下文特征向量表示，经余弦相似度和 k 近邻构建含 27,554 条边的加权网络，Louvain 分析得到 6 个社区（模块度约 0.495）和 27 个子社区。
正文 · 原文
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.
| Comments: | 32 pages, 8 tables, 13 figures |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.30885 [cs.CL] |
| (or arXiv:2610.30885v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.30885 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Rudra Pratap Deb Nath [view email]
[v1]
Thu, 22 Oct 2026 08:04:48 UTC (978 KB)
来源：arXiv cs.CL · arxiv.org