Public mental health through social media in the post COVID-19 era

Deepika Sharma; Jaiteg Singh; Babar Shah; Farman Ali; Ahmad Ali AlZubi; Mallak Ahmad AlZubi

doi:10.3389/fpubh.2023.1323922

Public mental health through social media in the post COVID-19 era

Front Public Health. 2023 Dec 11:11:1323922. doi: 10.3389/fpubh.2023.1323922. eCollection 2023.

Authors

Deepika Sharma^#¹, Jaiteg Singh¹, Babar Shah², Farman Ali^#³, Ahmad Ali AlZubi⁴, Mallak Ahmad AlZubi⁵

Affiliations

¹ Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
² College of Technological Innovation, Zayed University, Dubai, United Arab Emirates.
³ Department of Computer Science and Engineering, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea.
⁴ Department of Computer Science, Community College, King Saud University, Riyadh, Saudi Arabia.
⁵ Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.

^# Contributed equally.

Abstract

Social media is a powerful communication tool and a reflection of our digital environment. Social media acted as an augmenter and influencer during and after COVID-19. Many of the people sharing social media posts were not actually aware of their mental health status. This situation warrants to automate the detection of mental disorders. This paper presents a methodology for the detection of mental disorders using micro facial expressions. Micro-expressions are momentary, involuntary facial expressions that can be indicative of deeper feelings and mental states. Nevertheless, manually detecting and interpreting micro-expressions can be rather challenging. A deep learning HybridMicroNet model, based on convolution neural networks, is proposed for emotion recognition from micro-expressions. Further, a case study for the detection of mental health has been undertaken. The findings demonstrated that the proposed model achieved a high accuracy when attempting to diagnose mental health disorders based on micro-expressions. The attained accuracy on the CASME dataset was 99.08%, whereas the accuracy that was achieved on SAMM dataset was 97.62%. Based on these findings, deep learning may prove to be an effective method for diagnosing mental health conditions by analyzing micro-expressions.

Keywords: CNN; COVID-19; individual behavior; micro-expressions; public mental health; social media.

MeSH terms

COVID-19* / psychology
Emotions
Humans
Mental Health
Public Health
Social Media*

Grants and funding

The author (s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Researchers Supporting Project number (RSP2023R395), King Saud University, Riyadh, Saudi Arabia.