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A Two-Level Multi-Modal Analysis for Depression Detection From Online Social Media

A Two-Level Multi-Modal Analysis for Depression Detection From Online Social Media

Dhrubasish Sarkar, Piyush Kumar, Poulomi Samanta, Suchandra Dutta, Moumita Chatterjee
Copyright: © 2022 |Volume: 10 |Issue: 1 |Pages: 22
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781683182832|DOI: 10.4018/IJSI.309114
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MLA

Sarkar, Dhrubasish, et al. "A Two-Level Multi-Modal Analysis for Depression Detection From Online Social Media." IJSI vol.10, no.1 2022: pp.1-22. http://doi.org/10.4018/IJSI.309114

APA

Sarkar, D., Kumar, P., Samanta, P., Dutta, S., & Chatterjee, M. (2022). A Two-Level Multi-Modal Analysis for Depression Detection From Online Social Media. International Journal of Software Innovation (IJSI), 10(1), 1-22. http://doi.org/10.4018/IJSI.309114

Chicago

Sarkar, Dhrubasish, et al. "A Two-Level Multi-Modal Analysis for Depression Detection From Online Social Media," International Journal of Software Innovation (IJSI) 10, no.1: 1-22. http://doi.org/10.4018/IJSI.309114

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Abstract

According to World Health Organization statistics, depression is a prominent cause of concern worldwide, leading to suicide in the majority of these cases if left untreated. Nowadays, social media is a great place for users to express themselves through text, emoticons, images, etc., which reflect their thoughts and moods. This has opened up the possibility of studying social networks in order to better comprehend the mental states of their participants. The primary goal of the research is to examine Twitter user personas and tweets in order to uncover traits that may signal depressive symptoms among online users. A two-level depression detection method is proposed in which suspected depressed individuals are identified using social media features, personality traits, temporal and sentiment analysis of user biographies. Using the support vector machine classifier, these qualities are integrated with additional linguistic and topic features to achieve an accuracy of 89%. According to the research, effective feature selection and their combinations aid in enhancing performance.

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