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Tracking and Analyzing Public Emotion Evolutions During COVID-19: A Case Study from the Event-Driven Perspective on Microblogs

Author

Listed:
  • Qi Li

    (Beijing Key Lab of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China)

  • Cong Wei

    (Beijing Key Lab of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China)

  • Jianning Dang

    (Beijing Key Lab of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China)

  • Lei Cao

    (Department of Computer Science and Technology, Tsinghua University, Beijing 100085, China)

  • Li Liu

    (Beijing Key Lab of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China
    Current address: No. 19, Xinjiekouwai Street, HaiDian District, Beijing 100875, China.)

Abstract

Objective: Coronavirus disease 2019 (COVID-19) has caused substantial panic worldwide since its outbreak in December 2019. This study uses social networks to track the evolution of public emotion during COVID-19 in China and analyzes the root causes of these public emotions from an event-driven perspective. Methods: A dataset was constructed using microblogs (n = 125,672) labeled with COVID-19-related super topics (n = 680) from 40,891 users from 1 December 2019 to 17 February 2020. Based on the skeleton and key change points of COVID-19 extracted from microblogging contents, we tracked the public’s emotional evolution modes (accumulated emotions, emotion covariances, and emotion transitions) by time phase and further extracted the details of dominant social events. Results: Public emotions showed different evolution modes during different phases of COVID-19. Events about the development of COVID-19 remained hot, but generally declined, and public attention shifted to other aspects of the epidemic (e.g., encouragement, support, and treatment). Conclusions: These findings suggest that the public’s feedback on COVID-19 predated official accounts on the microblog platform. There were clear differences in the trending events that large users (users with many fans and readings) and common users paid attention to during each phase of COVID-19.

Suggested Citation

  • Qi Li & Cong Wei & Jianning Dang & Lei Cao & Li Liu, 2020. "Tracking and Analyzing Public Emotion Evolutions During COVID-19: A Case Study from the Event-Driven Perspective on Microblogs," IJERPH, MDPI, vol. 17(18), pages 1-24, September.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:18:p:6888-:d:416626
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    References listed on IDEAS

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    1. Pan, Junshan & Liu, Ying & Liu, Xiang & Hu, Hanping, 2016. "Discriminating bot accounts based solely on temporal features of microblog behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 193-204.
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    3. Yu‐Ru Lin & Drew Margolin & Xidao Wen, 2017. "Tracking and Analyzing Individual Distress Following Terrorist Attacks Using Social Media Streams," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1580-1605, August.
    4. Mukherjee, Shubhadeep & Bala, Pradip Kumar, 2017. "Sarcasm detection in microblogs using Naïve Bayes and fuzzy clustering," Technology in Society, Elsevier, vol. 48(C), pages 19-27.
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    Cited by:

    1. Hainan Huang & Weifan Chen & Tian Xie & Yaoyao Wei & Ziqing Feng & Weijiong Wu, 2021. "The Impact of Individual Behaviors and Governmental Guidance Measures on Pandemic-Triggered Public Sentiment Based on System Dynamics and Cross-Validation," IJERPH, MDPI, vol. 18(8), pages 1-25, April.

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