Unified Transformer Framework for Automated Cyberbullying Detection

Enas Alikhashashneh, Hedaia Alsawan, Khalid M. O. Nahar, Nahla Shatnawi, Ammar Almomani, Mohammad Alauthman, Shavi Bansal and Vincent Shin-Hung Pan
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Enas Alikhashashneh: Department of Information Systems, Yarmouk University, Jordan
Hedaia Alsawan: Department of Information Systems, Yarmouk University, Jordan
Khalid M. O. Nahar: Yarmouk University, Jordan
Nahla Shatnawi: Yarmouk University, Jordan
Ammar Almomani: Department of Computer Information Science, Higher Colleges of Technology, UAE
Mohammad Alauthman: Department of Information Security, University of Petra, Jordan
Shavi Bansal: Insights2Techinfo, India & University of Petroleum and Energy Studies, India
Vincent Shin-Hung Pan: Department of Information Management, Chaoyang University of Technology, Taiwan

International Journal of Cloud Applications and Computing (IJCAC), 2025, vol. 15, issue 1, 1-29

Abstract: Cyberbullying is a fast-growing public-health hazard, demanding reliable, real-time detection of abusive language online. This study presents a unified transformer framework that compares bidirectional encoder representations from transformers, generative pre-trained transformer-2 and text-to-text transfer transformer (T5) on the 90 356-message Mendeley Cyber-Bullying corpus. A shared pipeline normalises text, removes stop-words, and using T5, augments minority classes to curb imbalance. Models are fine-tuned under identical splits (70% train/15% val/15% test, 15 epochs) and scored with accuracy, precision, recall, and F1. Augmented T5 leads with 92.7% accuracy, surpassing generative pre-trained transformer-2 (90.1%) and bidirectional encoder representations from transformers (89.4%). Confusion-matrix analysis shows T5 best balances true- and false-positive rates. Results validate (a) casting cyberbullying detection as sequence-to-sequence; (b) transformer-driven augmentation as an efficient remedy for skewed data; and (c) the feasibility of lightweight, fine-tuned transformers for scalable safety tool.

Date: 2025
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