6个国家公众对COVID-19非药物干预措施的认知和态度:卡塔尔世界杯8强波胆分析Twitter数据的主题建模分析A Doogan,Caitlin,Wray, A Linger,Henry, A Brunt,Samantha +莫纳什大学信息技术学院数据科学与人工智能系，澳大利亚克莱顿惠灵顿路，3800,61 399031004，caitlin.doogan@monash.edu %K COVID-19 %K SARS-CoV-2 %K主题建模%K非药物干预%K社交媒体%K公共卫生%K机器学习%K社会距离%K封锁%K口罩%K信息流行病学%D 2020 %7 3.9.2020 %9背景:为减缓COVID-19的传播，世界各国政府已经实施了非药物干预(npi)(如戴口罩和保持社会距离)。为了促进公众对这些制度的遵守，政府需要了解公众对NPI制度的看法和态度以及影响它们的因素。Twitter数据提供了一种获取这些见解的方法。目的:本研究的目的是识别六个国家关于COVID-19 npi的推文，并比较这些国家公众对npi的看法和态度的趋势。目的是确定在2019冠状病毒病大流行的早期阶段影响公众对NPI制度的看法和态度的因素。方法:我们分析了来自6个国家(澳大利亚、加拿大、新西兰、爱尔兰、英国和美国)的777,869条关于COVID-19 npi的英语推文。使用Pearson相关分析评估tweet频率与病例数之间的关系。主题建模用于分离关于npi的推文。 A comparative analysis of NPIs between countries was conducted. Results: The proportion of NPI-related topics, relative to all topics, varied between countries. The New Zealand data set displayed the greatest attention to NPIs, and the US data set showed the lowest. The relationship between tweet frequencies and case numbers was statistically significant only for Australia (r=0.837, P<.001) and New Zealand (r=0.747, P<.001). Topic modeling produced 131 topics related to one of 22 NPIs, grouped into seven NPI categories: Personal Protection (n=15), Social Distancing (n=9), Testing and Tracing (n=10), Gathering Restrictions (n=18), Lockdown (n=42), Travel Restrictions (n=14), and Workplace Closures (n=23). While less restrictive NPIs gained widespread support, more restrictive NPIs were perceived differently across countries. Four characteristics of these regimes were seen to influence public adherence to NPIs: timeliness of implementation, NPI campaign strategies, inconsistent information, and enforcement strategies. Conclusions: Twitter offers a means to obtain timely feedback about the public response to COVID-19 NPI regimes. Insights gained from this analysis can support government decision making, implementation, and communication strategies about NPI regimes, as well as encourage further discussion about the management of NPI programs for global health events, such as the COVID-19 pandemic. %M 32784190 %R 10.2196/21419 %U //www.mybigtv.com/2020/9/e21419 %U https://doi.org/10.2196/21419 %U http://www.ncbi.nlm.nih.gov/pubmed/32784190