DefinePK

Abstract

The rapid growth of social media has higher concerns about its potential influence on mental health. Although social media may effect in a social connection, emotional support, etc; it has also been associated with bad significances, including anxiety, depression and stress. Nonetheless,  issue of determining particular mental health effects of social media behavior is not widely researched. The goal of this research is to model the predictions of depression, anxiety, and stress with the experience of deep-learning models trained on social media data and make comparisons with the predictions of other traditional statistical tools such as the regression analysis. The sample size was 500 participants of ages 18-30, including data about the use of social media (duration spent using social media, information interaction type: active and passive and interaction with the contents) and self-reported mental health results. The research used deep learning theories. It was revealed that passive use of social media (or scrolling with no action) was most associated with being linked with negative mental health outcomes whereas active use had some less strong effects. Furthermore, the models based on deep learning were more effective in comparison with the traditional ones, as the latter proved to have less predictive accuracy. This can be the result of the considerable improvement of the prediction of mental health outcomes with the assistance of the social media action as application of the deep learning models could be evaluated. The researcher furnished data on the effects of the trends of the social media usage on the mental health and need to facilitate the preservation of an active and healthy usage. The further studies need to concentrate on longitudinal research and use multimodal data to bias prediction model. The research paper is added to the developing sphere of computational mental health and suggests specific interventions via social media behaviors.