A Comparative Analysis of Transformer and Traditional ML Models for Cyberbullying Detection on Twitter (now X)
Muhammad Abusaqer, Jamil M. Saquer · 2025
Cyberbullying on social media poses critical risks to mental health and public safety. This paper investigates advanced computational approaches for detecting and classifying cyberbullying in a large Twitter dataset of 47,692 tweets, labeled into six categories. We compare three transformer-based models (GPT-3.5, BERT, and RoBERTa) against three traditional machine learning algorithms (Naïve Bayes, SVM, and Random Forest), evaluating accuracy, precision, recall, F1-score, and computational efficiency. Our results indicate that RoBERTa achieves the highest overall performance (87–88% accuracy) but at a higher computational cost (13 hours on CPU), while Random Forest offers a strong balance between speed and performance (85.36% accuracy in 83 seconds). In contrast, the experiment using GPT-3.5 in a batched, zero-shot configuration achieved accuracy of 25.41%, an F1-score of 23.61%, and elapsed time of 5.14 hours, highlighting the challenges of applying generative models to cyberbullying detection without fine-tuning. These findings inform model selection for real-world deployment of cyberbullying detection systems, illuminating the trade-offs between transformer-based and traditional methods for automated social media monitoring.