DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities

TL;DR

This paper introduces DEPTWEET, a new typology and dataset for detecting depression severity in social media texts, inspired by clinical assessment tools, and establishes baseline results using attention-based models.

Contribution

It develops a standardized depression typology for social media and provides a large annotated dataset with severity levels, enabling improved depression detection research.

Findings

Created a dataset of 40,191 tweets with expert annotations

Achieved baseline detection performance using BERT and DistilBERT models

Identified limitations and future directions for depression detection in social media

Abstract

Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data. In this study, we leverage the clinical articulation of depression to build a typology for social media texts for detecting the severity of depression. It emulates the standard clinical assessment procedure Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and Patient Health Questionnaire (PHQ-9) to encompass subtle indications of depressive disorders from tweets. Along with the typology, we present a new dataset of 40191 tweets labeled by expert annotators. Each tweet is labeled as 'non-depressed' or 'depressed'. Moreover, three severity levels are considered for 'depressed' tweets: (1) mild, (2) moderate, and (3) severe. An associated confidence score is provided with each label to validate the quality of annotation. We examine the quality of the dataset via representing summary statistics while setting strong baseline results using attention-based models like BERT and DistilBERT. Finally, we extensively address the limitations of the study to provide directions for further research.