AI detects cyberbullying early to support mental health care
Can Artificial Intelligence Support Healthcare and Mental Health Through Early Cyberbullying Detection ? The Impact of Emotion-Aware AI on Proactive Online Safety
Artificial IntelligenceComputation and Language
Summary
Cyberbullying and harmful online interactions can hurt people’s mental health and well-being. This paper shows a new tool called CareGuard that uses artificial intelligence to spot cyberbullying early by understanding the meaning and emotions in text. CareGuard combines smart language models with emotion filtering to focus on important content, helping healthcare workers watch for online risks. Tests show it finds harmful messages well while using computing resources efficiently, making it useful for health and safety monitoring.
What this means in practice
- •For healthcare data teams: Integrate AI tools to detect cyberbullying in patient communications for early mental health intervention.
- •For online safety moderators: Use the emotion-aware AI framework to proactively identify harmful messages for safer community management.
Authors
Hamed Jelodar, Amir Firouzi, Yen-Wu Lo, Maryam Tanha, Sajjad Dadkhah
Abstract
Healthcare systems, mental health, and public well-being are increasingly affected by cyberbullying and harmful online interactions. This paper presents CareGuard, an early-warning framework designed to support healthcare-driven mental health protection and proactive online safety through the detection of cyberbullying-related content using advanced natural language processing techniques. CareGuard integrates zero-shot semantic labeling with fine-tuned transformer-based models, including BERT, DistilBERT, and RoBERTa, to enable robust and context-aware classification across sensitive cyberbullying categories. To improve efficiency and reduce unnecessary computation in healthcare-oriented monitoring settings, the framework incorporates an emotion-aware filtering mechanism alongside cosine similarity-based semantic screening, allowing the system to focus on semantically relevant and emotionally salient content. Experimental results on benchmark datasets demonstrate that CareGuard effectively balances detection accuracy and computational efficiency, highlighting its potential for scalable deployment in healthcare systems, mental health monitoring, and online safety applications.