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

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.