RAPID: Leveraging Twitter Data for Real-time Public Health Responses to Coronavirus: Identifying Affective Desensitization, Loneliness and Depression, and Trust

该项目的目标是利用推特揭示与新冠肺炎传播相关的社会因素如何潜在地破坏公共卫生建议，同时也为公共卫生传播提供有关预防的信息。这项研究将导致(1)发展与大流行的社会影响和有效传播有关的新理论；(2)了解信息内容如何导致对这一现象的信任和不信任；(3)描述在大流行的不同阶段出现的广泛的公民叙述的特征；以及(4)直观地了解随着疾病在时间和空间上传播的社会因素的动态演变。结果将迅速传达给相关公共卫生机构，并将对使公共卫生传播在公民中更有效和更值得信赖具有价值。鉴于疾病的迅速传播，关键是立即确定社会影响，以便公共卫生机构和组织能够快速、动态和更有效地适应和反应，以建立信任。推特数据提供了一种手段来识别冠状病毒大流行的社会模式，因为它提供了公民反应的信号，以及在哪里需要卫生专业人员的指导，所有这些都是实时的。这项研究将使用关键字、机器学习、主题建模和定性分析，对与冠状病毒相关的两个大规模推文数据集进行持续数据收集。研究小组将增强一个基于地图的可视化系统，以显示不同地理区域随着时间的推移对研究问题的汇总和注释的社会反应。这将使研究人员能够深入了解此类社会现象明显的特定地区，并通过考虑与这些地区报告的冠状病毒病例相吻合的其他类型的信息来为分析提供信息。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this project is to use Twitter to uncover how social factors related to the spread of COVID-19 can potentially undermine public health recommendations but also inform public health communication about prevention. This research will lead to (1) the development of new theories related to social effects and effective communication of a pandemic, (2) understanding how message content leads to trust and distrust in the phenomenon; (3) characterizing widespread citizen narratives that emerge during different phases of the pandemic; and (4) visualizing how the dynamics of the social factors evolve as the disease spreads over time and space. The results will be swiftly communicated to relevant public health agencies, and will be valuable for making public health communications more effective and trustworthy among citizens. Given the rapid spread of the disease, it is critical to identify the social repercussions immediately so that public health agencies and organizations can adapt and respond quickly, dynamically, and more effectively to build trust. Twitter data provides a means to identify societal patterns of the coronavirus pandemic as it provides signals of citizen reactions and where there are needs for guidance from health professionals, all in real time. This research will use keyword, machine learning, topic modeling, and qualitative analysis on two large-scale datasets of tweets related to the coronavirus that involve ongoing data collection. The research team will enhance a map-based visualization system to show aggregated and annotated social responses from the research questions in different geographic regions over time. This will enable the researchers to gain insights about specific regions where such social phenomena are evident and can inform the analyses by considering other types of information that coincide with reported coronavirus cases in these regions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.