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Depression Detection Model in Social Network Content: A Survey

Depression Detection Model in Social Network Content: A Survey

Esraa M. Rabie, Atef F. Hashem, Fahad Kamal Alsheref
Copyright: © 2022 |Volume: 10 |Issue: 1 |Pages: 12
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781683182832|DOI: 10.4018/IJSI.309112
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MLA

Rabie, Esraa M., et al. "Depression Detection Model in Social Network Content: A Survey." IJSI vol.10, no.1 2022: pp.1-12. http://doi.org/10.4018/IJSI.309112

APA

Rabie, E. M., Hashem, A. F., & Alsheref, F. K. (2022). Depression Detection Model in Social Network Content: A Survey. International Journal of Software Innovation (IJSI), 10(1), 1-12. http://doi.org/10.4018/IJSI.309112

Chicago

Rabie, Esraa M., Atef F. Hashem, and Fahad Kamal Alsheref. "Depression Detection Model in Social Network Content: A Survey," International Journal of Software Innovation (IJSI) 10, no.1: 1-12. http://doi.org/10.4018/IJSI.309112

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Abstract

Social media (SM) is a platform that generates a massive quantity of data every day and allows individuals to engage with one another. For many people, social media has evolved into a way of life and the fifth component of daily living. Among the most popular social media platforms are Facebook, Instagram, Twitter, WhatsApp, and Snapchat. Depression is a frequent and dangerous medical condition that has a negative impact on how you feel, think, and act. A therapist's ability to swiftly detect depression in persons is limited since they cannot observe a person's mood throughout the day. Researchers can assess a person's sentiments via social media by looking at the user's posts and comments to discover if he or she has a mental issue. This survey study mirrors prior research on detecting depression using user-generated material from social media platforms, which introduce different techniques and compare between it to obtain accuracy.

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