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Review Open access Jul 2026

From Conversations to Insights: Analysing Social Networks for Early Mental Health Detection - A Systematic Review of Causal Inference and Deep Learning

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 · 0 citations
Review Jul 2026

Mental Health Disorder Detection Beyond Social Media: A Systematic Review of Available Datasets

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.

Sadiya Sayara Chowdhury Puspo, Ana-Maria Bucur, Stevie Chancellor et al. · 2 citations
Conference Open access 2026

Detection of Anxiety and Depression from Social Media Text Using Natural Language Processing

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. · 0 citations
Review

The African Journal of Information Systems The African

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
Open access Aug 2026

AI-Based Assessment of Mental Health Status in College Students through Social Network Data Analysis

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, Wangyang Ma, Guowei Yang et al. · 0 citations
Conference Jul 2026

A Web-Based Mental Health Assessment Framework using Natural Language Processing Techniques

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 Natural Language Processing (NLP) in real-time to analyze textual information provided by users. The offered system incorporates a hybrid deep learning framework with the relation of DeBERTa, BiLSTM, and XGBoost to promote the contextual comprehension, sequential emotional pattern identification, and effective classification of the performance. First, text input by the users is received with a secure web interface and processed with general NLP preprocessing methods, such as tokenization, lemmatization, and sentiment normalization. To extract deep semantic relationships in the text, DeBERTa is used to extract contextual embeddings. Such embeddings are also trained in the form of a Bidirectional Long Short-Term Memory (BiLSTM) network in order to capture emotional dynamics and linguistic reinforcing relations. Fused feature representation, sentiments, and linguistic indicators are input into an XGBoost classifier to predict mental health in multi-class. There is a weighted risk scoring system used to measure the level of severity and provide tailored feedback. It is experimentally tested on standard mental health text data sets that the proposed hybrid framework is more effective than the traditional machine learning and standalone transformer models in terms of accuracy, precision, recall, and F1-score. The architecture is scalable to the deployment of a web system, which is guaranteed to perform in real-time, secure data, and privacy of users. The suggested framework offers a solid and smart instrument to identify the risk of mental health early and help intervene in time and to promote the development of digital health care.

D. D, S. S, L. K et al. · 0 citations