发表机构
Faculty of Computer Science, University of New Brunswick; Khoury College of Computer Sciences, Northeastern University（新不伦瑞克大学计算机科学学院; 东北大学库里计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CareGuard框架，结合零样本语义标注与微调Transformer模型及情感感知过滤，实现高效准确的早期网络欺凌检测，以支持心理健康保护和主动在线安全。
AI 中文摘要
医疗保健系统、心理健康和公众福祉日益受到网络欺凌和有害在线互动的严重影响。本文提出了CareGuard，一个早期预警框架，旨在通过使用先进的自然语言处理技术检测与网络欺凌相关的内容，支持以医疗保健为导向的心理健康保护和主动在线安全。CareGuard将零样本语义标注与微调的基于Transformer的模型（包括BERT、DistilBERT和RoBERTa）相结合，以实现对敏感网络欺凌类别的稳健且上下文感知的分类。为了提高医疗保健导向监测环境中的效率并减少不必要的计算，该框架引入了情感感知过滤机制以及基于余弦相似度的语义筛选，使系统能够专注于语义相关且情感突出的内容。在基准数据集上的实验结果表明，CareGuard有效平衡了检测准确性和计算效率，突显了其在医疗保健系统、心理健康监测和在线安全应用中可扩展部署的潜力。
英文摘要
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.
