Source of Publication
Frontiers in Public Health
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.
Frontiers Media SA
Medicine and Health Sciences
public mental health, individual behavior, micro-expressions, COVID-19, social media, CNN
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Sharma, Deepika; Singh, Jaiteg; Shah, Babar; Ali, Farman; AlZubi, Ahmad Ali; and AlZubi, Mallak Ahmad, "Public mental health through social media in the post COVID-19 era" (2023). All Works. 6291.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series