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The delayed and combinatorial response of online public opinion to the real world: An inquiry into news texts during the COVID-19 era

Author

Listed:
  • Yamin Du

    (Huainan Normal University)

  • Huanhuan Cheng

    (Huainan Normal University)

  • Qing Liu

    (Huainan Normal University)

  • Song Tan

    (Huainan Normal University)

Abstract

In sociological research based on online public opinion, scholars often overlook the delay and combinatory nature of online responses to real-world events. This study aims to explore the delayed and combinatory responses of online public opinion to the intensity of the COVID-19 pandemic. Specifically, we seek to answer the following questions: (a) Is there a temporal delay in the response of online public opinion to the intensity of the pandemic? (b) Does this delay exhibit general characteristics of social networks, such as combinatory effects and higher-order interactions? To address these questions, we employ natural language processing techniques to extract online public opinion data and utilize statistical and machine learning-based causal inference methods for analysis. The findings indicate that online public opinion’s response to the intensity of COVID-19 is not immediate but rather exhibits a long-term lag. Identical COVID-19 intensity data can trigger multiple delayed public opinion responses, while a single delayed public opinion datum may be influenced by multiple preceding COVID-19 intensity data points. This delayed response of online public opinion and its higher-order network characteristics result in a waveform structure of real-world impacts influenced by online public opinion. We also utilized machine learning causal inference techniques to investigate the sensitivity differences in online public opinion responses to COVID-19 during various time periods.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03530-3
    DOI: 10.1057/s41599-024-03530-3
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    References listed on IDEAS

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    1. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    2. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    3. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    4. Drews, Stefan & Savin, Ivan & van den Bergh, Jeroen C.J.M. & Villamayor-Tomás, Sergio, 2022. "Climate concern and policy acceptance before and after COVID-19," Ecological Economics, Elsevier, vol. 199(C).
    5. Qingyuan Zhao & Trevor Hastie, 2021. "Causal Interpretations of Black-Box Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 272-281, January.
    6. 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.
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