Leveraging Large Language Models for Cyberbullying Prediction and Detection: A Multimodal Approach to Proactive Prevention and Policy Support.
Usman Butt, Nicholas Quinn, Sethuramalingam Subramaniam, Ammar Rashid · Procedia Computer Science · 2026
Cyberbullying (CB) continues to outpace effective prevention, intervention and aftercare measures posing a growing public health concern as digital systems become central to youth life. Persistent definitional ambiguities hinder evidence-based research and policy development. This study reviews these challenges and explores a technical solution using Large Language Models (LLMSs) for CB detection and prediction. Leveraging Google Gemini model, experiments evaluated text and image-based datasets for sentiments and behavioral analysis. LLM achieved 82% balanced accuracy in detection through zero shot learning and 77.8% in multimodal prediction, marking the first known exploration of this capability. Findings suggest that LLMs can enable flexible, scalable and proactive CB prevention strategies, offering a promising complement to existing policy and educational approaches.