Research Area: Machine Learning
Depression is common but serious mental disorder. It is classified as a mood disorder, which means that it is characterized by negative thoughts and emotions. With the development of Internet technology, more and more people post their life story and express their emotion on social media. Social media can provide a way to characterize and predict depression. It has been widely utilized by researchers to study mental health issues. However, most of the existing studies focus on textual data from social media. Few studies consider both text and image data. In this study, we aim to predict one’s depression tendency by analyzing image, text and behavior of his/her postings on Instagram. An effective mechanism is first employed to collect depressive and non-depressive user accounts. Next, three sets of features are extracted from image, text and behavior data to build the predictive deep learning model. We examine the potential for leveraging social media postings in understanding depress ion. Our experiment results demonstrate that the proposed model recognizes users who have depression tendency with an F-1 score of 82.3%. We are currently developing a tool based on this study for screening and detecting depression in an early stage.
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Author(s) Name: Yu Huang ; Chieh-Feng Chiang and Arbee Chen
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Conferrence name: In Proceedings of the 8th International Conference on Data Science, Technology and Applications - DATA
Publisher name: SCITEPRESS
DOI: 10.5220/0007833600320040
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