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

Author

Listed:
  • Suppawong Tuarob
  • Thanapon Noraset
  • Tanisa Tawichsri

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.

Suggested Citation

  • Suppawong Tuarob & Thanapon Noraset & Tanisa Tawichsri, 2022. "Using Large-Scale Social Media Data for Population-Level Mental Health Monitoring and Public Sentiment Assessment: A Case Study of Thailand," PIER Discussion Papers 169, Puey Ungphakorn Institute for Economic Research.
    • Handle: RePEc:pui:dpaper:169
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    References listed on IDEAS

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    2. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    3. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    4. Wingyan Chung & Daniel Zeng, 2016. "Social-media-based public policy informatics: Sentiment and network analyses of U.S. Immigration and border security," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(7), pages 1588-1606, July.
    5. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    6. Abeed Sarker & Karen O’Connor & Rachel Ginn & Matthew Scotch & Karen Smith & Dan Malone & Graciela Gonzalez, 2016. "Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter," Drug Safety, Springer, vol. 39(3), pages 231-240, March.
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    8. Suppawong Tuarob & Poom Wettayakorn & Ponpat Phetchai & Siripong Traivijitkhun & Sunghoon Lim & Thanapon Noraset & Tipajin Thaipisutikul, 2021. "DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-32, December.
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    More about this item

    Keywords

    Mental Health; Natural Language Processing; Deep Learning; Social Networks;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General

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    This paper has been announced in the following NEP Reports:

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