DEPTWEET: A typology for social media texts to detect depression severities. (February 2023)

• Record Type: Journal Article
• Authors: Kabir, Mohsinul; Ahmed, Tasnim; Hasan, Md. Bakhtiar; Laskar, Md Tahmid Rahman; Joarder, Tarun Kumar; Mahmud, Hasan; Hasan, Kamrul
• Is Part Of: Computers in human behavior. Volume 139(2023)
• Publication Date: 2023-02
• Subjects: Social media -- Mental health -- Depression severity -- Dataset; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2022.107503
• Languages: English
• ISSNs: 0747-5632

Abstract

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. Highlights: Tweets with depression symptoms were extracted based on clinical assessment tools. Collected tweets were annotated by trained annotators, supervised by domain experts. Four depression labels: None, Mild, Moderate, and Severe were added to each tweet. A dataset of 40191 tweets on severities of depression is made publicly available. The utility of the dataset was validated using statistical and mathematical modeling.