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The authors developed CareGuard, an early-warning NLP framework that couples zero-shot semantic labeling with fine-tuned transformer encoders (BERT, DistilBERT, and RoBERTa) to detect cyberbullying for proactive mental health protection. To resolve the computational bottlenecks of continuous text monitoring in public health contexts, the architecture implements an upstream emotion-aware and cosine-similarity semantic filter to discard non-salient interactions before heavy inference. Benchmark evaluations show that this staged gating strategy balances high context-aware detection accuracy with the computational efficiency required for scalable real-time deployment.
Pre-filtering text streams with emotion-aware semantic screening slashes the inference overhead of transformer-based toxicity classifiers without degrading cyberbullying detection sensitivity.
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.
