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Online public opinion during the first epidemic wave of COVID-19 in China based on Weibo data

Author

Listed:
  • Wen-zhong Shi

    (The Hong Kong Polytechnic University)

  • Fanxin Zeng

    (The Hong Kong Polytechnic University)

  • Anshu Zhang

    (The Hong Kong Polytechnic University)

  • Chengzhuo Tong

    (The Hong Kong Polytechnic University)

  • Xiaoqi Shen

    (China University of Mining and Technology)

  • Zhewei Liu

    (The Hong Kong Polytechnic University)

  • Zhicheng Shi

    (Shenzhen University)

Abstract

As COVID-19 spread around the world, epidemic prevention and control policies have been adopted by many countries. This process has prompted online social platforms to become important channels to enable people to socialize and exchange information. The massive use of social media data mining techniques, to analyze the development online of public opinion during the epidemic, is of great significance in relation to the management of public opinion. This paper presents a study that aims to analyze the developmental course of online public opinion in terms of fine-grained emotions presented during the COVID-19 epidemic in China. It is based on more than 45 million Weibo posts during the period from December 1, 2019 to April 30, 2020. A text emotion extraction method based on a dictionary of emotional ontology has been developed. The results show, for example, that a high emotional effect is observed during holidays, such as New Year. As revealed by Internet users, the outbreak of the COVID-19 epidemic and its rapid spread, over a comparatively short period of time, triggered a sharp rise in the emotion “fear”. This phenomenon was noted especially in Wuhan and the immediate surrounding areas. Over the initial 2 months, although this “fear” gradually declined, it remained significantly higher than the more common level of uncertainty that existed during the epidemic’s initial developmental era. Simultaneously, in the main city clusters, the response to the COVID-19 epidemic in central cities, was stronger than that in neighboring cities, in terms of the above emotion. The topics of Weibo posts, the corresponding emotions, and the analysis conclusions can provide auxiliary reference materials for the monitoring of network public opinion under similar major public events.

Suggested Citation

  • Wen-zhong Shi & Fanxin Zeng & Anshu Zhang & Chengzhuo Tong & Xiaoqi Shen & Zhewei Liu & Zhicheng Shi, 2022. "Online public opinion during the first epidemic wave of COVID-19 in China based on Weibo data," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-10, December.
    • Handle: RePEc:pal:palcom:v:9:y:2022:i:1:d:10.1057_s41599-022-01181-w
    DOI: 10.1057/s41599-022-01181-w
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    References listed on IDEAS

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    Cited by:

    1. Yamin Du & Huanhuan Cheng & Qing Liu & Song Tan, 2024. "The delayed and combinatorial response of online public opinion to the real world: An inquiry into news texts during the COVID-19 era," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.
    2. Bai, Congcong & Jin, Sheng & Jing, Jun & Yang, Chengcheng & Yao, Wenbin & Rong, Donglei & Alagbé, Jérémie Adjé, 2024. "A multimodal data-driven approach for driving risk assessment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).
    3. Zhihang Liu & Jinlin Wu & Connor Y. H. Wu & Xinming Xia, 2024. "Shifting sentiments: analyzing public reaction to COVID-19 containment policies in Wuhan and Shanghai through Weibo data," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    4. Meng Cai & Han Luo & Xiao Meng & Ying Cui & Wei Wang, 2022. "Influence of information attributes on information dissemination in public health emergencies," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-22, December.
    5. Shenzhen Tian & Jialin Jiang & Hang Li & Xueming Li & Jun Yang & Chuanglin Fang, 2023. "Flow space reveals the urban network structure and development mode of cities in Liaoning, China," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    6. Divine Q. Agozie & Muesser Nat, 2022. "Do communication content functions drive engagement among interest group audiences? An analysis of organizational communication on Twitter," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.

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