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Deriving anti-epidemic policy from public sentiment: A framework based on text analysis with microblog data

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  • Sijia Zhao
  • Lixuan Chen
  • Ying Liu
  • Muran Yu
  • Han Han

Abstract

Microblog has become the “first scenario” under which the public learn about the epidemic situation and express their opinions. Public sentiment mining based on microblog data can provide a reference for the government’s information disclosure, public sentiment guidance and formulation of epidemic prevention and control policy. In this paper, about 200,000 pieces of text data were collected from Jan. 1 to Feb. 26, 2020 from Sina Weibo, which is the most popular microblog website in China. And a public sentiment analysis framework suitable for Chinese-language scenarios was proposed. In this framework, a sentiment dictionary suitable for Chinese-language scenarios was constructed, and Baidu’s Sentiment Analysis API was used to calculate the public sentiment indexes. Then, an analysis on the correlation between the public sentiment indexes and the COVID-19 case indicators was made. It was discovered that there is a high correlation between public sentiments and incidence trends, in which negative sentiment is of statistical significance for the prediction of epidemic development. To further explore the source of public negative sentiment, the topics of the public negative sentiment on Weibo was analyzed, and 20 topics in five categories were got. It is found that there is a strong linkage between the hot spots of public concern and the epidemic prevention and control policies. If the policies cover the hot spots of public concern in a timely and effective manner, the public negative sentiment will be effectively alleviated. The analytical framework proposed in this paper also applies to the public sentiment analysis and policy making for other major public events.

Suggested Citation

  • Sijia Zhao & Lixuan Chen & Ying Liu & Muran Yu & Han Han, 2022. "Deriving anti-epidemic policy from public sentiment: A framework based on text analysis with microblog data," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-15, August.
    • Handle: RePEc:plo:pone00:0270953
    DOI: 10.1371/journal.pone.0270953
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    1. Cynthia Chew & Gunther Eysenbach, 2010. "Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-13, November.
    2. Sijia Li & Yilin Wang & Jia Xue & Nan Zhao & Tingshao Zhu, 2020. "The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users," IJERPH, MDPI, vol. 17(6), pages 1-9, March.
    3. Xuehua Han & Juanle Wang & Min Zhang & Xiaojie Wang, 2020. "Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China," IJERPH, MDPI, vol. 17(8), pages 1-22, April.
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