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ORIGINAL RESEARCH article

Front. Comput. Sci.
Sec. Human-Media Interaction
Volume 6 - 2024 | doi: 10.3389/fcomp.2024.1399395
This article is part of the Research Topic Unleashing the Power of Large Data: Models to Improve Individual Health Outcomes View all 6 articles

Development of a Two-Stage Depression Symptom Detection Model Using Twitter Data

Provisionally accepted
Faye Beatriz Tumaliuan Faye Beatriz Tumaliuan *Lorelie Grepo-Jalao Lorelie Grepo-Jalao Eugene Rex Jalao Eugene Rex Jalao
  • University of the Philippines Diliman, Quezon City, Philippines

The final, formatted version of the article will be published soon.

    This study aims to help in the area of depression screening in the Philippine setting, focusing on the detection of depression symptoms through language use and behavior in social media to help improve the accuracy of symptom tracking. A two-stage detection model is proposed, wherein the first stage deals with the detection if depression symptoms exist and the second stage focuses on the detection of depression symptom category or type for English and Filipino language. A baseline data set with 14 depression categories consisting of 86,163 tweets was used as input to various machine learning algorithms together with Twitter user behaviors, linguistic features, and psychological behaviors. The two-stage detection models used Bidirectional Long-Short Term Memory type of Artificial Neural Network with dropout nodes. The first stage, with a binary output classifier, can detect tweets with "Depression Symptom" or "No Symptom" categories with an accuracy of 0.91 and F1-score of 0.90. The second stage classifier has 6 depression symptom categories, namely "Mind and Sleep", "Appetite", "Substance use", "Suicidal tendencies", "Pain", and "Emotion" symptoms that has an accuracy of 0.83 and F1-score of 0.81. The two-stage algorithm can be used to complement mental health support provided by clinicians and in public health interventions to serve as high-level assessment tool. Limitations on misclassifications, negation, and data imbalance and biases can be addressed in future studies.

    Keywords: Depression detection, Social Media, Natural Language Processing, neural networks, Filipino

    Received: 11 Mar 2024; Accepted: 13 Nov 2024.

    Copyright: © 2024 Tumaliuan, Grepo-Jalao and Jalao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Faye Beatriz Tumaliuan, University of the Philippines Diliman, Quezon City, Philippines

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.