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Public mental health through social media in the post COVID-19 era
- Source :
- Frontiers in Public Health, Vol 11 (2023)
- Publication Year :
- 2023
- Publisher :
- Frontiers Media S.A., 2023.
-
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.
Details
- Language :
- English
- ISSN :
- 22962565
- Volume :
- 11
- Database :
- Directory of Open Access Journals
- Journal :
- Frontiers in Public Health
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.54a5cd0c77ae4ca8b13cd8742f0e2a53
- Document Type :
- article
- Full Text :
- https://doi.org/10.3389/fpubh.2023.1323922