Using Large-Scale Social Media Data for Population-Level Mental Health Monitoring and Public Sentiment Assessment: A Case Study of Thailand

Suppawong Tuarob, Thanapon Noraset and Tanisa Tawichsri

No 169, PIER Discussion Papers from Puey Ungphakorn Institute for Economic Research

Abstract: Mental health problems are among major public health concerns during the COVID-19 pandemic, given heightened uncertainties and drastic changes in lifestyles. However, mental health problem prevention and monitoring could be greatly improved given advancements in deep-learning techniques and readily available social media messages. This research uses deep learning algorithms to extract emotion, mood, and psychological cues from social media messages and then aggregates these signals to track population-level mental health. To verify the accuracy of our proposed approaches, we compared our findings to the actual number of patients treated for depression, attempted suicides, and self-harm cases reported by Thailand's Department of Mental Health. We discovered a strong correlation between the predicted mental signals and actual depression, suicide, and self-harm (injured) cases. Finally, we also create a database and user-friendly interface to facilitate researchers and policymakers to explore our extracted mental signals for further applications such as policy sentiment assessment.

Keywords: Mental Health; Natural Language Processing; Deep Learning; Social Networks (search for similar items in EconPapers)
JEL-codes: I10 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2022-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-sea
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