About: Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach

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About: Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach
Creator	Liu, Qian Huang, Jian Eysenbach, Gunther Ming, Wai-Kit Zhang, Casper Bahrami, Mohammad Akinwunmi, Babatunde Chen, Sihan Chen, Qiuyi Chu, Bojia Lamba, Manika Liu, Guan Osadchiy, Vadim Shi, Xiaochuan Zheng, Jiabin Zheng, Zequan Zhu, Hongyu
Source	Medline; PMC
abstract	BACKGROUND: In December 2019, a few coronavirus disease (COVID-19) cases were first reported in Wuhan, Hubei, China. Soon after, increasing numbers of cases were detected in other parts of China, eventually leading to a disease outbreak in China. As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus, such as its pathogenesis, spread, prevention, and containment. OBJECTIVE: The aim of this study was to collect media reports on COVID-19 and investigate the patterns of media-directed health communications as well as the role of the media in this ongoing COVID-19 crisis in China. METHODS: We adopted the WiseSearch database to extract related news articles about the coronavirus from major press media between January 1, 2020, and February 20, 2020. We then sorted and analyzed the data using Python software and Python package Jieba. We sought a suitable topic number with evidence of the coherence number. We operated latent Dirichlet allocation topic modeling with a suitable topic number and generated corresponding keywords and topic names. We then divided these topics into different themes by plotting them into a 2D plane via multidimensional scaling. RESULTS: After removing duplications and irrelevant reports, our search identified 7791 relevant news reports. We listed the number of articles published per day. According to the coherence value, we chose 20 as the number of topics and generated the topics’ themes and keywords. These topics were categorized into nine main primary themes based on the topic visualization figure. The top three most popular themes were prevention and control procedures, medical treatment and research, and global or local social and economic influences, accounting for 32.57% (n=2538), 16.08% (n=1258), and 11.79% (n=919) of the collected reports, respectively. CONCLUSIONS: Topic modeling of news articles can produce useful information about the significance of mass media for early health communication. Comparing the number of articles for each day and the outbreak development, we noted that mass media news reports in China lagged behind the development of COVID-19. The major themes accounted for around half the content and tended to focus on the larger society rather than on individuals. The COVID-19 crisis has become a worldwide issue, and society has become concerned about donations and support as well as mental health among others. We recommend that future work addresses the mass media’s actual impact on readers during the COVID-19 crisis through sentiment analysis of news data.
has issue date	2020-04-28(xsd:dateTime)
bibo:doi	10.2196/19118
bibo:pmid	32302966
has license	cc-by
schema:url	https://doi.org/10.2196/19118
resource representing a document's title	Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach
has PubMed Central identifier	PMC7189789
has PubMed identifier	32302966
schema:publication	J Med Internet Res
resource representing a document's body	covid:PMC7189789#body_text
is schema:about of	named entity 'TOPIC' named entity 'DIGITAL' named entity 'News Media' named entity 'COVID' named entity 'TikTok' named entity 'leading indicator' named entity 'medical institutions' named entity 'infectious disease' named entity 'pneumonia' named entity 'pneumonia' named entity 'COVID' named entity 'Hubei' named entity 'TF-IDF' named entity 'coronaviruses' named entity 'co-occurrence' named entity 'disease outbreaks' named entity 'China' named entity 'COVID-19' named entity 'medical staff' named entity 'coronavirus disease' named entity 'LDA' named entity 'mass media' named entity 'lockdown' named entity 'virus' named entity 'COVID-19' named entity 'medical treatment' named entity 'COVID' named entity 'sentiment analysis' named entity 'health information' named entity 'Japan' named entity 'generative statistical model' named entity 'virus' named entity 'semantic' named entity 'mass media' named entity 'public health' named entity 'COVID-19' named entity 'Chinese government' named entity 'mental health problems' named entity 'virus' named entity 'United States' named entity 'mass media' named entity 'COVID-19 vaccine' named entity 'multidimensional scaling' named entity 'COVID' named entity 'topic modeling' named entity 'prevention and control' named entity 'Viruses' named entity 'Chinese government' named entity 'semantic similarity' named entity 'control methods' named entity 'topic modeling' named entity 'Wuhan' named entity 'China' named entity 'Prevention and control' named entity 'coronavirus' named entity 'medical field' named entity 'prevention and control' named entity 'coronavirus' named entity 'Python' named entity 'COVID' named entity 'hierarchical Bayesian model' named entity 'Thailand' named entity 'health information' named entity 'Python Software Foundation' named entity 'viral infections' named entity 'Zhejiang University'

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