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Exploring Future Signals of COVID-19 and Response to Information Diffusion Using Social Media Big Data

Author

Listed:
  • Juyoung Song

    (Criminal Justice, Pennsylvania State University, Schuylkill, PA 17972, USA)

  • Dal-Lae Jin

    (Department of Public Health, Graduate School of Korea University & Transdisciplinary Major in Learning Health Systems, Korea University, Seoul 02841, Republic of Korea)

  • Tae Min Song

    (School of Industry and Environment, Gachon University, Seoul 13120, Republic of Korea)

  • Sang Ho Lee

    (CEO for HealthMax Co., Ltd., Seoul 06078, Republic of Korea)

Abstract

COVID-19 is a respiratory infectious disease that first reported in Wuhan, China, in December 2019. With COVID-19 spreading to patients worldwide, the WHO declared it a pandemic on 11 March 2020. This study collected 1,746,347 tweets from the Korean-language version of Twitter between February and May 2020 to explore future signals of COVID-19 and present response strategies for information diffusion. To explore future signals, we analyzed the term frequency and document frequency of key factors occurring in the tweets, analyzing the degree of visibility and degree of diffusion. Depression, digestive symptoms, inspection, diagnosis kits, and stay home obesity had high frequencies. The increase in the degree of visibility was higher than the median value, indicating that the signal became stronger with time. The degree of visibility of the mean word frequency was high for disinfectant, healthcare, and mask. However, the increase in the degree of visibility was lower than the median value, indicating that the signal grew weaker with time. Infodemic had a higher degree of diffusion mean word frequency. However, the mean degree of diffusion increase rate was lower than the median value, indicating that the signal grew weaker over time. As the general flow of signal progression is latent signal → weak signal → strong signal → strong signal with lower increase rate, it is necessary to obtain active response strategies for stay home, inspection, obesity, digestive symptoms, online shopping, and asymptomatic.

Suggested Citation

  • Juyoung Song & Dal-Lae Jin & Tae Min Song & Sang Ho Lee, 2023. "Exploring Future Signals of COVID-19 and Response to Information Diffusion Using Social Media Big Data," IJERPH, MDPI, vol. 20(9), pages 1-11, May.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:9:p:5753-:d:1142077
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    References listed on IDEAS

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