类型学
社会化媒体
杠杆(统计)
萧条(经济学)
心理学
心理健康
人工智能
计算机科学
自然语言处理
机器学习
精神科
万维网
社会学
人类学
宏观经济学
经济
作者
Mohsinul Kabir,Tasnim Ahmed,Md. Bakhtiar Hasan,Md Tahmid Rahman Laskar,Tarun Kumar Joarder,Hasan Mahmud,Kamrul Hasan
标识
DOI:10.1016/j.chb.2022.107503
摘要
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. • 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.
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