Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions

Fuente: arXiv

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Bibliographic Details
Main Authors:	Aggarwal, Vaishali, Thukral, Sachin, Patel, Krushil, Chatterjee, Arnab
Format:	Preprint
Published:	2025
Subjects:	Social and Information Networks Artificial Intelligence Information Retrieval
Online Access:	Acceder al recurso
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author	Aggarwal, Vaishali Thukral, Sachin Patel, Krushil Chatterjee, Arnab
author_facet	Aggarwal, Vaishali Thukral, Sachin Patel, Krushil Chatterjee, Arnab
contents	Textual data from social platforms captures various aspects of mental health through discussions around and across issues, while users reach out for help and others sympathize and offer support. We propose a comprehensive framework that leverages Natural Language Processing (NLP) and Generative AI techniques to identify and assess mental health disorders, detect their severity, and create recommendations for behavior change and therapeutic interventions based on users' posts on Reddit. To classify the disorders, we use rule-based labeling methods as well as advanced pre-trained NLP models to extract nuanced semantic features from the data. We fine-tune domain-adapted and generic pre-trained NLP models based on predictions from specialized Large Language Models (LLMs) to improve classification accuracy. Our hybrid approach combines the generalization capabilities of pre-trained models with the domain-specific insights captured by LLMs, providing an improved understanding of mental health discourse. Our findings highlight the strengths and limitations of each model, offering valuable insights into their practical applicability. This research potentially facilitates early detection and personalized care to aid practitioners and aims to facilitate timely interventions and improve overall well-being, thereby contributing to the broader field of mental health surveillance and digital health analytics.
format	Preprint
id	arxiv_https___arxiv_org_abs_2503_01442
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions Aggarwal, Vaishali Thukral, Sachin Patel, Krushil Chatterjee, Arnab Social and Information Networks Artificial Intelligence Information Retrieval Textual data from social platforms captures various aspects of mental health through discussions around and across issues, while users reach out for help and others sympathize and offer support. We propose a comprehensive framework that leverages Natural Language Processing (NLP) and Generative AI techniques to identify and assess mental health disorders, detect their severity, and create recommendations for behavior change and therapeutic interventions based on users' posts on Reddit. To classify the disorders, we use rule-based labeling methods as well as advanced pre-trained NLP models to extract nuanced semantic features from the data. We fine-tune domain-adapted and generic pre-trained NLP models based on predictions from specialized Large Language Models (LLMs) to improve classification accuracy. Our hybrid approach combines the generalization capabilities of pre-trained models with the domain-specific insights captured by LLMs, providing an improved understanding of mental health discourse. Our findings highlight the strengths and limitations of each model, offering valuable insights into their practical applicability. This research potentially facilitates early detection and personalized care to aid practitioners and aims to facilitate timely interventions and improve overall well-being, thereby contributing to the broader field of mental health surveillance and digital health analytics.
title	Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions
topic	Social and Information Networks Artificial Intelligence Information Retrieval
url	https://arxiv.org/abs/2503.01442