Searcharxiv⌕ Search

arXiv · 2610.30885

From Perpetrators' Digital Footprints to Behavioral Communities: An Ontology-Driven Framework for Bengali Cyberbullying Analysis--- FULL VERSION

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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Md. Nesarul Hoque, Md Hanif Seddiqui, Miskatul Anwara, Rudra Pratap Deb Nath. 2026-10-22. From Perpetrators' Digital Footprints to Behavioral Communities: An Ontology-Driven Framework for Bengali Cyberbullying Analysis--- FULL VERSION. https://arxiv.org/abs/2610.30885

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Kongzi: A Historical Large Language Model with Fact Enhancement

Large Language Models (LLMs) can exhibit factual inaccuracies during long-chain reasoning or under misleading prompts, limiting their reliability in historical analysis. We propose Kongzi, a language model for historical understanding and reasoning that combines continued pre-training on historical corpora, supervised fine-tuning, and fact-enhanced reinforcement learning. Its factuality reward compares entities and relations extracted from generated responses with historical references through knowledge-graph similarity. We evaluate 0.5B and 7B models on historical question answering and counterfactual reasoning, using reported ratings from three LLM judges, and on CMMLU-Chinese-History. Kongzi-7B receives the highest historical-accuracy ratings among the compared models under all three judges. Reinforcement learning improves the mean answer scores of both model sizes and their CMMLU-Chinese-History accuracy, although Kongzi-7B remains below the strongest reported baselines on that benchmark. These descriptive results support further study of factuality rewards for historical language models.

cs.CL↗

ShIOEnv: A Command Evaluation Environment for Grammar-Constrained Synthesis and Execution Behavior Modeling

We introduce ShIOEnv, a Gymnasium-compatible Bash environment for synthesizing execution-annotated shell input traces. Datasets containing such traces are useful for training models to perform execution-free shell simulation, which in turn is useful for deploying honeypots for cyber deception. Existing datasets, such as NL2CMD and other Bash command corpora, do not include complete execution artifacts (stdout, stderr, exit status, and changes to the system's state) associated with a command's execution. ShIOEnv synthesizes shell inputs, executes them in a controlled system context, and records stdout, stderr, exit status, and structured filesystem and environment patches. Unconstrained (random) generation of shell inputs (command and arguments) results in multiple instances of arguments that are mostly redundant or result in execution errors. We introduce grammar-constrained synthesis to generate syntactically valid arguments with high input irreducibility, which we estimate through self-ablative contrasts of execution artifacts. We prepare 1.6M samples using grammar-constrained and unconstrained synthesis and collect their execution artifacts for 86 Linux/Bash utilities. Using this dataset, we train small language models (CodeT5) and evaluate their accuracy in predicting the actual execution behavior of attacker-sourced Bash commands. These models outperform commonly used rule-based simulation (Cowrie) and prompted large language models by up to 14.2 pp in output edit similarity (O-ES). We enhance the NL2CMD dataset by constructing 500K samples executable in a grounded system context along with their complete execution artifacts. We empirically demonstrate that models trained on grammar-constrained samples outperform those trained on NL2CMD samples by 22.5 pp in O-ES. ShIOEnv, command grammars, and 2.1M samples used in our primary evaluations are released for use by other researchers.

cs.CL↗

How Well Does Mathematical Reasoning Transfer Across Languages in English-Centric LLMs?

Recent advances in Reinforcement Post-Training (RPT) have substantially improved the mathematical reasoning capabilities of large language models (LLMs), but it remains unclear how well such gains transfer across languages. In this work, we study cross-lingual mathematical reasoning transfer in English-centric LLMs under controlled multilingual evaluation. We first evaluate a range of English-centric reasoning models on multilingual mathematical reasoning benchmarks and find that transfer is highly variable across initial model choice, target language, and training paradigm. Through controlled comparative experiments, we further find that stronger English-centric initialization does not necessarily lead to stronger relative transfer efficiency, even when it yields higher multilingual reasoning accuracy. Finally, we show that the largest improvement comes from moving beyond English-only supervision: adding a single parallel language yields a substantial gain, while further gains from additional languages are smaller but consistent. Overall, our results highlight the need to evaluate multilingual mathematical reasoning directly, using both absolute performance and relative transfer-efficiency metrics.

cs.CL↗