Citation

Malhotra A, Gupta M, Gupta V, Shokeen S, Singla A. Int. J. Adv. Trends Comput. Sci. Eng. 2022; 11(1): 14-19.

Copyright

(Copyright © 2022, World Academy of Research in Science and Engineering)

DOI

10.30534/ijatcse/2022/041112022

PMID

unavailable

Abstract

Depression is a major disorder in the population of the 21st century. Previous studies have associated depression with internet usage and as the access to internet spreads through the developing countries depression can prove to be a challenging issue to combat. This paper proposes a new method to detect the presence of general depression among social media users using features extracted from their online habits including browsing, streaming, games and social media. The pertinent data has been collected from individuals primarily based in developing countries and applied various established supervised machine learning algorithms to predict the presence of general depression. Preliminary testing shows that the proposed system performs rather well and enables an easy method for keeping online mental health in check without compromising on privacy. The proposed work shows a clear positive correlation between social media usage and general depression highlighting the inimical effects of elevated usage.

Language: en