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Threshold of Depression Measure in the Framework of Sentiment Analysis of Tweets: Managing Risk during a Crisis Period Like the COVID-19 Pandemic

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  • Jules Clement Mba

    (School of Economics, University of Johannesburg, P.O. Box 524 Auckland Park, Johannesburg 2006, South Africa)

  • Mduduzi Biyase

    (School of Economics, University of Johannesburg, P.O. Box 524 Auckland Park, Johannesburg 2006, South Africa)

Abstract

The COVID-19 pandemic has had a devastating impact on the world. The surge in the number of daily new cases and deaths around the world and in South Africa, in particular, has increased fear, psychological breakdown, and uncertainty among the population during the COVID-19 pandemic period, leading many to resort to prayer, meditation, and the consumption of religious media as coping measures. This study analyzes social media data to examine the perceptions and attitudes of the South African community toward religion as well as their well-being appreciation during the COVID-19 period. We extract four sets of tweets related to COVID-19, religion, life purpose, and life experience, respectively, by users within the geographical area of South Africa and compute their sentiment scores. Then, a Granger causality test is conducted to assess the causal relationship between the four time series. While the findings reveal that religious sentiment scores Granger-causes life experience, COVID-19 similarly Granger-causes life experience, illustrating some shifts experienced within the community during the crisis. This study further introduces for the first time a Threshold of Depression measure in the sentiment analysis framework to assist in managing the risk induced by extremely negative sentiment scores. Risk management during a period of crisis can be a hectic task, especially the level of distress or depression the community is experiencing in order to offer adequate mental support. This can be assessed through the Conditional Threshold of Depression which quantifies the threshold of depression of a community conditional on a given variable being at its Threshold of Depression. The findings indicate that the well-being indicators (life purpose and life experience) provide the highest values of this threshold and could be used to monitor the emotions of the population during periods of crisis to support the community in crisis management.

Suggested Citation

  • Jules Clement Mba & Mduduzi Biyase, 2023. "Threshold of Depression Measure in the Framework of Sentiment Analysis of Tweets: Managing Risk during a Crisis Period Like the COVID-19 Pandemic," JRFM, MDPI, vol. 16(2), pages 1-12, February.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:2:p:115-:d:1065369
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    References listed on IDEAS

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    1. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    2. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
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    Cited by:

    1. Hannah Michelle Russell & Donald L. Ariail & Katherine Taken Smith & Lawrence Murphy Smith, 2023. "Religiosity and Risk: Association of Judeo-Christian Ethicality with a Sustainable Business Environment," JRFM, MDPI, vol. 16(9), pages 1-19, September.

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