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A Study on Epidemic Information Screening, Prevention and Control of Public Opinion Based on Health and Medical Big Data: A Case Study of COVID-19

Author

Listed:
  • Jinhai Li

    (College of Information Engineering, Taizhou University, Taizhou 225300, China)

  • Yunlei Ma

    (Department of Personnel, Taizhou University, Taizhou 225300, China)

  • Xinglong Xu

    (School of Management, Jiangsu University, Zhenjiang 212013, China)

  • Jiaming Pei

    (School of Computer Science, The University of Sydney, Camperdown, NSW 2006, Australia)

  • Youshi He

    (School of Management, Jiangsu University, Zhenjiang 212013, China)

Abstract

The outbreak of the coronavirus disease 2019 (COVID-19) represents an alert for epidemic prevention and control in public health. Offline anti-epidemic work is the main battlefield of epidemic prevention and control. However, online epidemic information prevention and control cannot be ignored. The aim of this study was to identify reliable information sources and false epidemic information, as well as early warnings of public opinion about epidemic information that may affect social stability and endanger the people’s lives and property. Based on the analysis of health and medical big data, epidemic information screening and public opinion prevention and control research were decomposed into two modules. Eight characteristics were extracted from the four levels of coarse granularity, fine granularity, emotional tendency, and publisher behavior, and another regulatory feature was added, to build a false epidemic information identification model. Five early warning indicators of public opinion were selected from the macro level and the micro level to construct the early warning model of public opinion about epidemic information. Finally, an empirical analysis on COVID-19 information was conducted using big data analysis technology.

Suggested Citation

  • Jinhai Li & Yunlei Ma & Xinglong Xu & Jiaming Pei & Youshi He, 2022. "A Study on Epidemic Information Screening, Prevention and Control of Public Opinion Based on Health and Medical Big Data: A Case Study of COVID-19," IJERPH, MDPI, vol. 19(16), pages 1-21, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:16:p:9819-:d:883939
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    References listed on IDEAS

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    1. Valerio Basile & Francesco Cauteruccio & Giorgio Terracina, 2021. "How Dramatic Events Can Affect Emotionality in Social Posting: The Impact of COVID-19 on Reddit," Future Internet, MDPI, vol. 13(2), pages 1-32, January.
    2. Yolanda Eraso & Stephen Hills, 2021. "Intentional and unintentional non-adherence to social distancing measures during COVID-19: A mixed-methods analysis," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-29, August.
    3. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    4. Daesik Kim & Chung Joo Chung & Kihong Eom, 2022. "Measuring Online Public Opinion for Decision Making: Application of Deep Learning on Political Context," Sustainability, MDPI, vol. 14(7), pages 1-16, March.
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    Cited by:

    1. Jun Liu & Shuang Lai & Ayesha Akram Rai & Abual Hassan & Ray Tahir Mushtaq, 2023. "Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace," IJERPH, MDPI, vol. 20(5), pages 1-24, February.

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