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新冠疫情期间中国谣言的传播与驳斥:信息流行病学研究。

Dissemination and Refutation of Rumors During the COVID-19 Outbreak in China: Infodemiology Study.

机构信息

Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.

School of Public Health, Fudan University, Shanghai, China.

出版信息

J Med Internet Res. 2021 Feb 15;23(2):e22427. doi: 10.2196/22427.


DOI:10.2196/22427
PMID:33493124
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7886374/
Abstract

BACKGROUND: During the outbreak of COVID-19, numerous rumors emerged on the internet in China and caused confusion among the public. However, the characteristics of these rumors in different phases of the epidemic have not been studied in depth, and the official responses to the rumors have not been systematically evaluated. OBJECTIVE: The aims of this study were to evaluate the rumor epidemic and official responses during the COVID-19 outbreak in China and to provide a scientific basis for effective information communication in future public health crises. METHODS: Data on internet rumors related to COVID-19 were collected via the Sina Weibo Official Account to Refute Rumors between January 20 and April 8, 2020, extracted, and analyzed. The data were divided into five periods according to the key events and disease epidemic. Different classifications of rumors were described and compared over the five periods. The trends of the epidemic and the focus of the public at different stages were plotted, and correlation analysis between the number of rumors and the number of COVID-19 cases was performed. The geographic distributions of the sources and refuters of the rumors were graphed, and analyses of the most frequently appearing words in the rumors were applied to reveal hotspots of the rumors. RESULTS: A total of 1943 rumors were retrieved. The median of the response interval between publication and debunking of the rumors was 1 day (IQR 1-2). Rumors in text format accounted for the majority of the 1943 rumors (n=1241, 63.9%); chat tools, particularly WeChat (n=1386, 71.3%), were the most common platform for initial publishing of the rumors (n=1412, 72.7%). In addition to text rumors, Weibo and web pages were more likely to be platforms for rumors released in multimedia formats or in a combination of formats, respectively. Local agencies played a large role in dispelling rumors among social media platforms (1537/1943, 79.1%). There were significant differences in the formats and origins of rumors over the five periods (P<.001). Hubei Province accounted for most of the country's confirmed rumors. Beijing and Wuhan City were the main centers for debunking of disinformation. The words most frequently included in the core messages of the rumors varied by period, indicating shifting in the public's concern. CONCLUSIONS: Chat tools, particularly WeChat, became the major sources of rumors during the COVID-19 outbreak in China, indicating a requirement to establish rumor monitoring and refuting mechanisms on these platforms. Moreover, targeted policy adjustments and timely release of official information are needed in different phases of the outbreak.

摘要

背景:在 COVID-19 爆发期间,中国互联网上出现了大量谣言,给公众造成了困惑。然而,这些谣言在疫情不同阶段的特征尚未得到深入研究,官方对谣言的回应也未得到系统评估。

目的:本研究旨在评估 COVID-19 在中国爆发期间的谣言流行情况和官方回应,并为未来突发公共卫生事件中进行有效的信息传播提供科学依据。

方法:2020 年 1 月 20 日至 4 月 8 日期间,通过新浪微博官方辟谣账号收集了与 COVID-19 相关的网络谣言数据,并对其进行了提取和分析。根据关键事件和疾病流行情况,将数据分为五个时期。描述并比较了五个时期不同分类的谣言。绘制了不同阶段的疫情趋势和公众关注点,并对谣言数量和 COVID-19 病例数量进行了相关性分析。绘制了谣言来源和辟谣者的地理分布,并对谣言中最常出现的词语进行了分析,以揭示谣言的热点。

结果:共检索到 1943 条谣言。谣言发布和辟谣之间的响应间隔中位数为 1 天(IQR 1-2)。在 1943 条谣言中,文本格式的谣言占多数(n=1241,63.9%);聊天工具,尤其是微信(n=1386,71.3%)是最初发布谣言的最常见平台(n=1412,72.7%)。除了文本谣言,微博和网页分别更有可能成为发布多媒体格式或混合格式谣言的平台。社交媒体平台上的辟谣工作主要由地方机构承担(1537/1943,79.1%)。五个时期谣言的格式和来源存在显著差异(P<.001)。湖北省是中国确诊谣言的主要来源地。北京和武汉市是辟谣的主要中心。谣言核心信息中最常包含的词语因时期而异,表明公众关注点的变化。

结论:在中国 COVID-19 爆发期间,聊天工具,尤其是微信,成为谣言的主要来源,这表明需要在这些平台上建立谣言监测和辟谣机制。此外,还需要在疫情爆发的不同阶段进行有针对性的政策调整和及时发布官方信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4ba/7886374/8f24185f19ac/jmir_v23i2e22427_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4ba/7886374/8375ba5c122d/jmir_v23i2e22427_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4ba/7886374/1012bf2bae42/jmir_v23i2e22427_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4ba/7886374/1f3cd68d3e12/jmir_v23i2e22427_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4ba/7886374/de1d962ae272/jmir_v23i2e22427_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4ba/7886374/8f24185f19ac/jmir_v23i2e22427_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4ba/7886374/8375ba5c122d/jmir_v23i2e22427_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4ba/7886374/1012bf2bae42/jmir_v23i2e22427_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4ba/7886374/1f3cd68d3e12/jmir_v23i2e22427_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4ba/7886374/de1d962ae272/jmir_v23i2e22427_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4ba/7886374/8f24185f19ac/jmir_v23i2e22427_fig5.jpg

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[4]
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[7]
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[9]
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[10]
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