Development of a two-stage depression symptom detection model: application of neural networks to Twitter data - Department of Industrial Engineering & Operations Research

RESEARCH | Depression is a health condition characterized by changes in mood, cognition, and behavior. According to the World Health Organization (WHO), the Philippines had one of the highest rates of depression in 2017, affecting 3.3 million Filipinos. WHO projects that by 2030, depression will become the leading contributor to the global burden of disease.

This study developed a depression detection tool through language and behavior in social media. Since language can reveal how a person feels, this research built a tool that looks at online posts, specifically Twitter (now called X), to identify signs of depression more accurately using machine learning methods.

A data set composed of 86,163 tweets categorized according to depression symptoms was used to train the model. Use of words, how people behave on Twitter, and signs of psychological distress were also considered. The first stage classifies a tweet as showing “Depression Symptom” or “No Symptom.” If a symptom is present, it is further classified into any of these six categories in the second stage: “Mind and Sleep,” “Appetite,” “Substance use,” “Suicidal tendencies,” “Pain,” and “Emotion”. The output of the two-stage model resulted in accuracies of 91% for stage 1 and 83% for stage 2.

This tool can be used to complement mental health support provided by clinicians to screen for potential symptoms and can be useful for public health efforts to reach more people who may need help.

Authors: Faye Beatriz Tumaliuan, Lorelie Grepo and Eugene Rex Jalao (all from the Department of Industrial Engineering and Operations Research, College of Engineering, University of the Philippines Diliman)

Published in Frontiers in Computer Science

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