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Can Artificial Intelligence Support Healthcare and Mental Health Through Early Cyberbullying Detection ? The Impact of Emotion-Aware AI on Proactive Online Safety — arXiv:2609.09735

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.09735
License (as archived): CC0-1.0
Archived: 2026-10-16T07:24:31.016Z
SHA-256 of the archived text: 1151fb20bb539295bdf86dc557b1a0b99b2f32156684cc7a7b3cb9b3cce21b16
Stable URL of this page: https://metraipolis.com/documento/dc_ec74ed589aba

How to cite

Can Artificial Intelligence Support Healthcare and Mental Health Through Early Cyberbullying Detection ? The Impact of Emotion-Aware AI on Proactive Online Safety — arXiv:2609.09735 — arXiv (en), https://arxiv.org/abs/2609.09735 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ec74ed589aba (Metraipolis archive dc_ec74ed589aba, sha256 1151fb20bb539295bdf86dc557b1a0b99b2f32156684cc7a7b3cb9b3cce21b16)

BibTeX

@misc{dcec74ed589aba,
  title = {Can Artificial Intelligence Support Healthcare and Mental Health Through Early Cyberbullying Detection ? The Impact of Emotion-Aware AI on Proactive Online Safety — arXiv:2609.09735},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.09735},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ec74ed589aba; sha256 1151fb20bb539295bdf86dc557b1a0b99b2f32156684cc7a7b3cb9b3cce21b16},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.09735. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-10 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.09735 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T07:00:16.212Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-10?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).