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. 2020 Nov 17;8(4):691.
doi: 10.3390/vaccines8040691.

Characterizing News Report of the Substandard Vaccine Case of Changchun Changsheng in China: A Text Mining Approach

Ping Zhou  1 , Yao He  2 , Chao Lyu  1 , Xiaoguang Yang  1
Affiliations

Characterizing News Report of the Substandard Vaccine Case of Changchun Changsheng in China: A Text Mining Approach

Ping Zhou et al. Vaccines (Basel). .

Abstract

Background: The substandard vaccine case of that broke out in July 2018 in China triggered an outburst of news reports both domestically and aboard. Distilling the abundant textual information is helpful for a better understanding of the character during this public event. Methods: We collected the texts of 2211 news reports from 83 mainstream media outlets in China between 15 July and 25 August 2018, and used a structural topic model (STM) to identify the major topics and features that emerged. We also used dictionary-based sentiment analysis to uncover the sentiments expressed by the topics as well as their temporal variations. Results: The main topics of the news report fell into six major categories, including: (1) Media Investigation, (2) Response from the Top Authority, (3) Government Action, (4) Knowledge Dissemination, (5) Finance Related and (6) Commentary. The topic prevalence shifted during different stages of the events, illustrating the actions by the government. Sentiments generally spanned from negative to positive, but varied according to different topics. Conclusion: The characteristics of news reports on vaccines are shaped by various topics at different stages. The inner dynamics of the topic and its alterations are driven by the interaction between social sentiment and governmental intervention.

Keywords: news report; sentiment analysis; substandard vaccine case; text mining; topic model.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Textual data generative mechanism of the Topic Model [33].
Figure 2
Figure 2
Temporal trends of the news reports included in the study with the corresponding Google trends and Weixin index, indicating the social attention to this incident.
Figure 3
Figure 3
Prevalence and temporal variation of the topics. Note: The dotted line is the proportion of the highest prevalence topics at a certain time point.
Figure 4
Figure 4
Time trends of the topic categories.
Figure 5
Figure 5
Daily sentiment score of the news.
Figure 6
Figure 6
Sentiment scores of the 17 topics.
Figure 7
Figure 7
Sentiment words used most frequently in the corpus.
Figure 8
Figure 8
Temporal changes in the contribution of various sentiment words.
See this image and copyright information in PMC

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