Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

TL;DR

This paper presents a semi-supervised method for detecting clinical depression symptoms from Twitter data, achieving over 68% accuracy, offering a non-intrusive alternative to traditional surveys for mental health monitoring.

Contribution

It introduces a semi-supervised statistical model that correlates Twitter language patterns with clinical depression symptoms aligned with PHQ-9 criteria.

Findings

Detection accuracy of 68%

Precision of 72% in identifying depressive symptoms

Aligns social media language with clinical assessments

Abstract

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.