From Conversations to Insights: Analysing Social Networks for Early Mental Health Detection - A Systematic Review of Causal Inference and Deep Learning
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Abstract
Early detection of mental health issues is crucial for timely intervention, reducing the severity of conditions, and improving overall well-being. Social networks have emerged as valuable platforms for identifying mental health issues, thanks to user-generated content and social interactions. Artificial intelligence (AI), especially causal inference and deep learning, has great potential for analysing large-scale social media data. It helps identify patterns and relationships that enable earlier and more accurate prediction of mental health issues. The paper aims to systematically review academic articles on the applications of AI, including deep learning and causal inference in social networks for early mental health detection. The systematic review initially considered 1,018 academic articles retrieved from major scholarly databases, including IEEE Xplore, Scopus, and ScienceDirect. The review was conducted in accordance with the PRISMA framework to ensure a transparent and rigorous screening and selection process. After careful review, the articles were filtered down to 90 for full analysis to present a classification framework based on four dimensions: Applications in mental health, methods/techniques, datasets used, and challenges. It was identified that AI continues to significantly outperform humans in terms of accuracy, efficiency, and early intervention for mental health detection. Methods and techniques map directly to relevant keywords such as AI applications, causal inference, deep learning, and social networks. Although AI shows great promise in mental health detection, challenges such as data bias, privacy risks, and a lack of model interpretability remain. Combining causal inference with deep learning can create personalised mental health interventions. Still, future research must prioritise explainable AI, privacy-preserving methods, and ethical data collection to ensure responsible, transparent AI applications in mental health care.
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