标题： 基于社交媒体互动的数据驱动的机构层面风险与响应沟通的推断 显示英文标题

标题： Data-driven Inferences of Agency-level Risk and Response Communication on COVID-19 through Social Media based Interactions

摘要： 公共机构通过社交媒体进行的风险和响应沟通在新型冠状病毒（COVID-19）的出现和传播中发挥了重要作用，这种互动也在其他信息渠道中得到回应。 本研究收集了时效性的在线社交媒体数据，并使用数据驱动的方法分析了公共卫生（WHO、CDC）、紧急事务（FEMA）和交通（FDOT）机构的此类沟通模式。 本研究的范围包括深入了解机构在大流行期间如何通过社交媒体传播风险信息并影响社区响应（即封锁时间、重新开放时间）和疾病爆发指标（即确诊人数、死亡人数）。 数据包括不同机构的Twitter互动（每机构平均2.15K条推文）以及在2020年2月21日至2020年6月6日期间观察到的新冠病例和死亡人数的众包数据（即Worldometer）。 这里应用了多种机器学习技术，如（例如）主题挖掘和随时间变化的情感评分，以识别这一前所未有的时期内新兴主题的动态变化。 结果的时间信息图捕捉了不同机构在关于佩戴口罩的重要性、居家隔离、社交距离和接触者追踪等信息传播中的变化。 此外，机构在讨论社区传播、个人防护设备短缺、检测和医疗物资、烟草使用、疫苗、心理健康问题、住院情况、飓风季节、机场、建筑工作等方面存在差异。 研究结果可支持在社区转向新常态以及未来大流行期间更高效地传递风险和响应信息。 显示英文摘要

摘要： Risk and response communication of public agencies through social media played a significant role in the emergence and spread of novel Coronavirus (COVID-19) and such interactions were echoed in other information outlets. This study collected time-sensitive online social media data and analyzed such communication patterns from public health (WHO, CDC), emergency (FEMA), and transportation (FDOT) agencies using data-driven methods. The scope of the work includes a detailed understanding of how agencies communicate risk information through social media during a pandemic and influence community response (i.e. timing of lockdown, timing of reopening) and disease outbreak indicators (i.e. number of confirmed cases, number of deaths). The data includes Twitter interactions from different agencies (2.15K tweets per agency on average) and crowdsourced data (i.e. Worldometer) on COVID-19 cases and deaths were observed between February 21, 2020 and June 06, 2020. Several machine learning techniques such as (i.e. topic mining and sentiment ratings over time) are applied here to identify the dynamics of emergent topics during this unprecedented time. Temporal infographics of the results captured the agency-levels variations over time in circulating information about the importance of face covering, home quarantine, social distancing and contact tracing. In addition, agencies showed differences in their discussions about community transmission, lack of personal protective equipment, testing and medical supplies, use of tobacco, vaccine, mental health issues, hospitalization, hurricane season, airports, construction work among others. Findings could support more efficient transfer of risk and response information as communities shift to new normal as well as in future pandemics.