It was identified that AI continues to significantly outperform humans in terms of accuracy, efficiency, and early intervention for mental health detection.
U. Nazir, Nur Shazwani Kamarudin, Mazlina Abdul Majid· Journal of Communication, La...· 0 citations
It is found that non-social media free-text based datasets are predominantly focused on English and on detecting depression, and this first comprehensive review of non-social media, free-text datasets for mental health research is presented.
A comprehensive Natural Language Processing (NLP) pipeline for detecting linguistic correlates of anxiety and depression from social media text, a task distinct from clinical diagnosis is presented.
Muhammad Azhar, Adeen Amjad, Bilal Hussain et al.· International Conference on...· 0 citations
The findings reveal that social media usage exacerbates mental health issues such as depression, anxiety, fear of missing out (FOMO), social and financial comparisons, and educating users on healthy social media habits and the early signs of mental health distress is critical.
R. Paper, Ebrahim Timol, Kebashnee Moodley· 0 citations
AI-based analysis of social network data provides an effective, non-invasive method for assessing college students' mental health status, offering significant potential for improving mental health monitoring and prevention strategies in educational settings.
Yin Zhi, Wang-Yang Ma, Guo-Wei Yang et al.· Schizophrenia bulletin· 0 citations
Mental illnesses like depression, anxiety, stress, and so on have become more widespread, and this has necessitated the availability of assessment tools that are readily available, scalable, and automated. The paper will offer a web-based mental health risk assessment system that utilizes the latest technology of Natur...
D. D, S. S, L. K et al.· 2026 4th International Confe...· 0 citations
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