TY  - EJOU
AU  - Sayfudin,  
AU  - Stiawan, Deris 
AU  - Ferdiansyah,  
AU  - Budiarto, Rahmat 

TI  - Automated Hate Speech Profiling via Lexicon-Enriched Ensemble Learning and Ego-Network Analysis
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - Tightening global regulation of digital toxicity demands hate-speech detection that is accurate, explainable, traceable, and forensically usable. The challenge intensifies in multilingual and code-mixed settings such as Indonesian social media, where linguistic variation and informal expressions cause feature sparsity and reduce machine learning (ML) effectiveness. Most prior work emphasizes text classification while neglecting actor profiling and the network structures through which hate speech propagates. We propose Dynamic Lexicon-Driven Network (DyLex-Net), an integrated framework for profiling actors who disseminate hate speech, combining dataset-driven dynamic-lexicon analysis, classical ML ensemble validation, and ego-network analysis under a forensic-readiness orientation. The lexicon is built from a large multilingual corpus and serves as a transparent, auditable knowledge base for real-time inference. Logistic Regression (LR), Linear Support Vector Machine (SVM), and a voting ensemble are used for offline benchmarking. Experiments cover an integrated corpus of more than 715,000 posts plus real-time account-level inference. The framework achieves consistent F1 across models, with ensembles most stable. DyLex-Net produces explainable, traceable actor risk profiles that satisfy both analytical accuracy and forensic interpretability, bridging technical performance and legal requirements for multilingual hate-speech analysis and contributing to cyber threat intelligence and digital forensics.
KW  - Hate speech detection; cyber threat intelligence; actor profiling; forensic readiness; dynamic lexicon; ego-network analysis; code-mixed text; ensemble learning

DO  - 10.32604/cmc.2026.084177