Dataset Information

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

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.

SUBMITTER: Zhou P

PROVIDER: S-EPMC7711875 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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

Zhou Ping P He Yao Y Lyu Chao C Yang Xiaoguang X

Vaccines 20201117 4

<i>Background:</i> 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. <i>Methods</i>: 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 ...[more]

PMID: 33212869

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Project description:BackgroundCOVID-19 is still rampant all over the world. Until now, the COVID-19 vaccine is the most promising measure to subdue contagion and achieve herd immunity. However, public vaccination intention is suboptimal. A clear division lies between medical professionals and laypeople. While most professionals eagerly promote the vaccination campaign, some laypeople exude suspicion, hesitancy, and even opposition toward COVID-19 vaccines.ObjectiveThis study aims to employ a text mining approach to examine expression differences and thematic disparities between the professionals and laypeople within the COVID-19 vaccine context.MethodsWe collected 3196 answers under 65 filtered questions concerning the COVID-19 vaccine from the China-based question and answer forum Zhihu. The questions were classified into 5 categories depending on their contents and description: adverse reactions, vaccination, vaccine effectiveness, social implications of vaccine, and vaccine development. Respondents were also manually coded into two groups: professional and laypeople. Automated text analysis was performed to calculate fundamental expression characteristics of the 2 groups, including answer length, attitude distribution, and high-frequency words. Furthermore, structural topic modeling (STM), as a cutting-edge branch in the topic modeling family, was used to extract topics under each question category, and thematic disparities were evaluated between the 2 groups.ResultsLaypeople are more prevailing in the COVID-19 vaccine-related discussion. Regarding differences in expression characteristics, the professionals posted longer answers and showed a conservative stance toward vaccine effectiveness than did laypeople. Laypeople mentioned countries more frequently, while professionals were inclined to raise medical jargon. STM discloses prominent topics under each question category. Statistical analysis revealed that laypeople preferred the "safety of Chinese-made vaccine" topic and other vaccine-related issues in other countries. However, the professionals paid more attention to medical principles and professional standards underlying the COVID-19 vaccine. With respect to topics associated with the social implications of vaccines, the 2 groups showed no significant difference.ConclusionsOur findings indicate that laypeople and professionals share some common grounds but also hold divergent focuses toward the COVID-19 vaccine issue. These incongruities can be summarized as "qualitatively different" in perspective rather than "quantitatively different" in scientific knowledge. Among those questions closely associated with medical expertise, the "qualitatively different" characteristic is quite conspicuous. This study boosts the current understanding of how the public perceives the COVID-19 vaccine, in a more nuanced way. Web-based question and answer forums are a bonanza for examining perception discrepancies among various identities. STM further exhibits unique strengths over the traditional topic modeling method in statistically testing the topic preference of diverse groups. Public health practitioners should be keenly aware of the cognitive differences between professionals and laypeople, and pay special attention to the topics with significant inconsistency across groups to build consensus and promote vaccination effectively.

Project description:BackgroundBefore the advent of an effective vaccine, nonpharmaceutical interventions, such as mask-wearing, social distancing, and lockdowns, have been the primary measures to combat the COVID-19 pandemic. Such measures are highly effective when there is high population-wide adherence, which requires information on current risks posed by the pandemic alongside a clear exposition of the rules and guidelines in place.ObjectiveHere we analyzed online news media coverage of COVID-19. We quantified the total volume of COVID-19 articles, their sentiment polarization, and leading subtopics to act as a reference to inform future communication strategies.MethodsWe collected 26 million news articles from the front pages of 172 major online news sources in 11 countries (available online at SciRide). Using topic detection, we identified COVID-19-related content to quantify the proportion of total coverage the pandemic received in 2020. The sentiment analysis tool Vader was employed to stratify the emotional polarity of COVID-19 reporting. Further topic detection and sentiment analysis was performed on COVID-19 coverage to reveal the leading themes in pandemic reporting and their respective emotional polarizations.ResultsWe found that COVID-19 coverage accounted for approximately 25.3% of all front-page online news articles between January and October 2020. Sentiment analysis of English-language sources revealed that overall COVID-19 coverage was not exclusively negatively polarized, suggesting wide heterogeneous reporting of the pandemic. Within this heterogenous coverage, 16% of COVID-19 news articles (or 4% of all English-language articles) can be classified as highly negatively polarized, citing issues such as death, fear, or crisis.ConclusionsThe goal of COVID-19 public health communication is to increase understanding of distancing rules and to maximize the impact of governmental policy. The extent to which the quantity and quality of information from different communication channels (eg, social media, government pages, and news) influence public understanding of public health measures remains to be established. Here we conclude that a quarter of all reporting in 2020 covered COVID-19, which is indicative of information overload. In this capacity, our data and analysis form a quantitative basis for informing health communication strategies along traditional news media channels to minimize the risks of COVID-19 while vaccination is rolled out.

Dataset Information

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

Publications

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

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