AI-Powered Frameworks for the Detection and Prevention of Cyberbullying Across Social Media Ecosystems

Mondol ; Md. Anas ; Uddaula ; Md. Ashaf ; Hossain ; Md. Safaet ; Siddika ; Mst. Ayesha
TechComp Innovations: Journal of Computer Science... 📅 2025 Vol 2, No 1 🔗 DOI: 10.70063/techcompinnovations.v2i1.59
Abstract
This study presents an advanced AI-powered framework to detect and prevent cyberbullying across diverse social media platforms using a multiclass classification approach. Addressing the growing complexity and linguistic diversity of online abuse, the research integrates various machine learning (RF, LR, SVM) and deep learning (Bi-LSTM, BERT) models trained on a balanced dataset covering bullying categories based on religion, age, ethnicity, gender, and neutral content. Data preprocessing, tokenization, feature extraction via TF-IDF and CountVectorizer, and class balancing using SMOTE were applied to enhance model accuracy. The proposed system further supports real-time detection through social media APIs, offering dynamic monitoring and intervention capabilities. Among the tested models, Random Forest and BERT achieved the highest classification performance with 94% accuracy. Despite its robust architecture, limitations include dependence on English-language datasets, exclusion of multimodal data (e.g., memes, audio), and API restrictions that challenge scalability. Future development will focus on incorporating vision-language models and optimizing the system for real-time, multilingual, and multimodal environments. This study contributes to digital safety efforts by proposing a scalable and adaptive detection system suitable for safeguarding users from evolving forms of online harassment.
Keywords
Cyberbullying Detection; Text Classification; Machine Learning; Deep Learning; Social Media
How to Cite

Mondol, et al. (2025). AI-Powered Frameworks for the Detection and Prevention of Cyberbullying Across Social Media Ecosystems. TechComp Innovations: Journal of Computer Science and Technology, 2(1). https://doi.org/10.70063/techcompinnovations.v2i1.59

Mondol, Md. Anas; Uddaula, Md. Ashaf; Hossain, Md. Safaet; Siddika, Mst. Ayesha, "AI-Powered Frameworks for the Detection and Prevention of Cyberbullying Across Social Media Ecosystems," TechComp Innovations: Journal of Computer Science and Technology, vol. 2, no. 1, 2025.

Mondol, Md. Anas; Uddaula, Md. Ashaf; Hossain, Md. Safaet; Siddika, Mst. Ayesha. "AI-Powered Frameworks for the Detection and Prevention of Cyberbullying Across Social Media Ecosystems." TechComp Innovations: Journal of Computer Science and Technology, vol. 2, no. 1, 2025.

Mondol, Md. Anas; Uddaula, Md. Ashaf; Hossain, Md. Safaet; Siddika, Mst. Ayesha. "AI-Powered Frameworks for the Detection and Prevention of Cyberbullying Across Social Media Ecosystems." TechComp Innovations: Journal of Computer Science and Technology 2, no. 1 (2025).

Mondol, et al. (2025) 'AI-Powered Frameworks for the Detection and Prevention of Cyberbullying Across Social Media Ecosystems', TechComp Innovations: Journal of Computer Science and Technology, 2(1). doi: 10.70063/techcompinnovations.v2i1.59.

Mondol, Md. Anas; Uddaula, Md. Ashaf; Hossain, Md. Safaet; Siddika, Mst. Ayesha. AI-Powered Frameworks for the Detection and Prevention of Cyberbullying Across Social Media Ecosystems. TechComp Innovations: Journal of Computer Science and Technology. 2025;2(1).

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