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Akkapon Wongkoblap¹

¹Institute of Digital Arts and Science, Suranaree University of Technology, Thailand.

Studies in Health Technology and Informatics

|June 30, 2023

Summary

This summary is machine-generated.

This study introduces a method to detect depression in social media users, achieving 97% accuracy. This approach aids in identifying individuals with mental health conditions through online self-disclosures.

Keywords:

Data collection Depression Machine learning Social media

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Area of Science:

Digital mental health
Computational psychiatry
Social media analytics

Background:

Mental illness is a growing global concern requiring effective diagnostic tools.
Accurate diagnosis of mental health disorders relies heavily on comprehensive patient history and symptom data.
Social media platforms offer a unique avenue for observing user self-disclosure related to mental health.

Purpose of the Study:

To propose and evaluate an automated method for collecting data from social media users who disclose symptoms of depression.
To assess the efficacy of social media data analysis in identifying potential cases of depression.

Main Methods:

Development of an automated data collection system targeting social media posts.
Analysis of user-generated content for indicators of depression.
Validation of the proposed method's accuracy in identifying depression disclosure.

Main Results:

The proposed method achieved a high accuracy rate of 97% in identifying depression disclosure.
A majority of 95% confidence was associated with the accuracy of the findings.
Demonstrated the feasibility of using social media data for mental health screening.

Conclusions:

Automated analysis of social media self-disclosure presents a promising tool for mental health research and early detection of depression.
This approach can supplement traditional diagnostic methods by providing real-time insights.
Further research can refine this method for broader mental health condition identification.