Detection of Severity of Depression from Social Media Data using Attention-Based Approach

Kabir, Md. Mohsinul

dc.contributor.author	Kabir, Md. Mohsinul
dc.date.accessioned	2023-04-27T09:58:47Z
dc.date.available	2023-04-27T09:58:47Z
dc.date.issued	2022-05-30
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dc.identifier.uri	http://hdl.handle.net/123456789/1854
dc.description	Supervised by Dr. Md. Kamrul Hasan, Professor, Department of Computer Science and Engineering(CSE), Islamic University of Technology (IUT) Board Bazar, Gazipur-1704, Bangladesh. This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2022.	en_US
dc.description.abstract	In clinical psychology, diagnostic of mental illness is mostly done by patient’s self reported experiences, behaviors reported by the patients themselves, their relatives and a mental status examination. This method can lead to a variety of biases, such as cognitive bias, in which patients hide their illness for fear of judgement. Popular social networks can serve as a tool for dealing with this problem. But mental health research in this domain has been hindered by a lack of standard typology, scarcity of adequate data and lack of a robust classification network. In this thesis, the clinical articulation of depression is leveraged 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 social media texts. The typology has been developed with the association of two expert psychologists. To examine the typology, a dataset is constructed by scraping posts from Twitter, followed by a standard annotation method to label each tweet as ‘non-depressed’ or ‘depressed’, while three severity levels are considered for ‘depressed’ tweets: (1) mild, (2) moderate, and (3) severe. To classify severity of depression in this dataset, two attention-based models, namely BERT and DistilBERT are pre-trained and fine-tuned and a strong baseline result is provided. The findings of this study ought to provide strong directions for further research in this domain.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh	en_US
dc.subject	Social Media, Mental Health, Depression Severity, Typology, Attention	en_US
dc.title	Detection of Severity of Depression from Social Media Data using Attention-Based Approach	en_US
dc.type	Thesis	en_US