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

RAPID：利用 Twitter 数据对冠状病毒进行实时公共卫生反应：识别情感脱敏、孤独和抑郁以及信任

• 批准号: 2027254
• 负责人: Gloria Mark
• 金额: $ 18万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027254&HistoricalAwards=false
• 关键词: RAPID; Leveraging; Twitter; Data; Real

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.

该项目的目的是使用Twitter来揭示与19岁的传播有关的社会因素如何可能破坏公共卫生建议，同时也为公共卫生沟通提供有关预防的信息。 这项研究将导致（1）与社会效应和大流行有效交流有关的新理论的发展，（2）了解信息内容如何导致对现象的信任和不信任； （3）表征在大流行阶段的不同阶段出现的广泛公民叙事； （4）可视化社会因素的动态如何随着时间和空间的蔓延而演变。 结果将迅速传达给相关的公共卫生机构，并将对公共卫生的公民通讯更加有效和值得信赖，这将是有价值的。 鉴于该疾病的迅速传播，至关重要的是要立即确定社会影响，以便公共卫生机构和组织可以适应和反应，动态，动态，更有效地建立信任。 Twitter数据提供了一种识别冠状病毒大流行的社会模式的方法，因为它提供了公民反应的信号，以及在卫生专业人员需要实时提供指导的地方。 这项研究将对与涉及持续数据收集的冠状病毒有关的两个大规模的推文数据集使用关键字，机器学习，主题建模和定性分析。研究团队将增强基于地图的可视化系统，以显示随着时间的推移，不同地理区域中研究问题的汇总和注释的社会响应。这将使研究人员能够了解这种社会现象是显而易见的特定地区的见解，并可以通过考虑与这些区域中报告的冠状病毒案例相吻合的其他类型的信息来为分析提供信息。该奖项反映了NSF的法定任务，并通过基金会的知识优点和广泛的影响来评估NSF的法定任务，并被认为是值得的支持。