Department of Civil and Environmental Engineering, College of Engineering and Computing, Florida Interna-tional University, Miami, Florida.
Moss Department of Construction Management, Moss School of Construction, Infrastructure and Sustainability, College of Engineering and Computing, Florida International University, Miami, Florida.
J Emerg Manag. 2021;19(7):59-82. doi: 10.5055/jem.0589.
Risk perception and risk averting behaviors of public agencies in the emergence and spread of COVID-19 can be retrieved through online social media (Twitter), and such interactions can be echoed in other information outlets. This study collected time-sensitive online social media data and analyzed patterns of health risk communication of public health and emergency agencies in the emergence and spread of novel coronavirus using data-driven methods. The major focus is toward understanding how policy-making agencies communicate risk and response information through social media during a pandemic and influence community response-ie, timing of lockdown, timing of reopening, etc.-and disease outbreak indicators-ie, number of confirmed cases and number of deaths. Twitter data of six major public organizations (1,000-4,500 tweets per organization) are collected from February 21, 2020 to June 6, 2020. Several machine learning algorithms, including dynamic topic model and sentiment analysis, are applied over time to identify the topic dynamics over the specific timeline of the pandemic. Organizations emphasized on various topics-eg, importance of wearing face mask, home quarantine, understanding the symptoms, social distancing and contact tracing, emerging community transmission, lack of personal protective equipment, COVID-19 testing and medical supplies, effect of tobacco, pandemic stress management, increasing hospitalization rate, upcoming hurricane season, use of convalescent plasma for COVID-19 treatment, maintaining hygiene, and the role of healthcare podcast in different timeline. The findings can benefit emergency management, policymakers, and public health agencies to identify targeted information dissemination policies for public with diverse needs based on how local, federal, and international agencies reacted to COVID-19.
公共机构在 COVID-19 出现和传播过程中的风险感知和避险行为可以通过在线社交媒体（Twitter）检索到，这些交互可以在其他信息来源中得到回应。本研究通过数据驱动的方法收集了时间敏感的在线社交媒体数据，并分析了公共卫生和应急机构在新型冠状病毒出现和传播过程中的健康风险沟通模式。主要关注点是了解决策机构如何通过社交媒体在大流行期间传达风险和应对信息，并影响社区的反应，即封锁的时间、重新开放的时间等，以及疾病爆发指标，即确诊病例数和死亡人数。从 2020 年 2 月 21 日到 2020 年 6 月 6 日，收集了六个主要公共组织（每个组织 1000-4500 条推文）的 Twitter 数据。应用了几种机器学习算法，包括动态主题模型和情感分析，随着时间的推移来识别特定大流行时间表上的主题动态。组织强调了各种主题，例如佩戴口罩的重要性、居家隔离、了解症状、保持社交距离和追踪接触者、出现社区传播、缺乏个人防护设备、COVID-19 检测和医疗用品、烟草的影响、大流行压力管理、住院率增加、即将到来的飓风季节、使用恢复期血浆治疗 COVID-19、保持卫生以及医疗保健播客在不同时间线上的作用。这些发现可以使应急管理、政策制定者和公共卫生机构受益，根据地方、联邦和国际机构对 COVID-19 的反应，为具有不同需求的公众确定有针对性的信息传播政策。
