TY - JOUR AU - Liu, Qian AU - Zheng, ze泉AU - Zheng，嘉彬AU - Chen，秋义AU - Liu，关AU - Chen，思汉AU - Chu，伯嘉AU - Zhu，宏宇AU - Akinwunmi，巴巴通德AU - Huang, Jian AU - Zhang, Casper J P AU Ming, Wai-Kit PY - 2020 DA - 20/4/28 TI -中国COVID-19暴发早期新闻媒体健康传播:数字主题建模方法JO - J Med Internet Res SP - e19118 VL - 22 IS - 4kw -冠状病毒KW - COVID-19 KW -疫情KW -健康传播KW -大众媒体KW -公共危机KW -主题建模AB -背景:2019年12月，中国湖北省武汉市首次报告了一些冠状病毒疾病(COVID-19)病例。不久之后，中国其他地区也发现了越来越多的病例，最终导致了疾病在中国的爆发。随着这种可怕的疾病迅速传播，大众媒体积极开展COVID-19社区教育，传播有关这种新型冠状病毒的发病机制、传播方式、预防和遏制等健康信息。目的:本研究的目的是收集有关COVID-19的媒体报道，并调查媒体导向的健康传播模式，以及媒体在中国当前COVID-19危机中的作用。方法:我们采用WiseSearch数据库，从2020年1月1日至2020年2月20日期间的主要媒体中提取有关冠状病毒的相关新闻文章。然后我们使用Python软件和Python包Jieba对数据进行整理和分析。我们寻求一个合适的主题数与连贯数的证据。我们使用合适的主题编号进行潜狄利克雷分配主题建模，生成相应的关键字和主题名称。然后，我们通过多维缩放将这些主题划分为不同的主题。 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. SN - 1438-8871 UR - //www.mybigtv.com/2020/4/e19118/ UR - https://doi.org/10.2196/19118 UR - http://www.ncbi.nlm.nih.gov/pubmed/32302966 DO - 10.2196/19118 ID - info:doi/10.2196/19118 ER -