HNDS-I: A Data Visualization Tool for the COVID-19 Online Prevalence of Emotions in Institutions Database

COVID-19机构情绪在线流行率（COPE-ID）数据库包含有关COVID-19的在线讨论，包括有关情绪（如恐惧、焦虑）和社会机构（如医疗保健和家庭）的帖子。这些数据可用于回答有关COVID-19大流行期间信息传播和个人福祉的问题。该项目创建了一个数据可视化工具来处理COPE-ID中的社交媒体数据。该工具使人们更容易探索大量的社交媒体数据，以研究流行病或相关灾难期间人们的情绪，思想，行为和健康状况。可视化工具允许所有背景和技能水平的研究人员访问和处理来自COPE-ID的数据，以及来自其他社交媒体来源的数据。改善COPE-ID数据的获取可以为未来的公共卫生政策和干预措施提供信息。 数据可视化工具的用户将能够通过平台仪表板访问大型社交媒体数据集的概述。仪表板显示由主题建模算法构建的数据的可视化，这些算法以单词和主题频率的形式生成数据的摘要。该工具还允许用户进行情感分析，例如对主题的态度从消极到积极。诸如词云和时间序列图表之类的可视化为用户生成洞察力，以推动他们与该工具进行基于任务的交互。用户还可以请求可以使用定性或内容分析进行标记的数据样本。然后，这些标记的数据可以用于对未来事件进行预测，这些预测是由高级统计分析或机器学习技术生成的。训练数据集可用于编码和处理COPE-ID数据，这些编码数据集可用于检查编码器之间的一致率，从而提高数据的质量。该工具改善了科学家对社交媒体数据的访问，并允许研究人员使用用户生成的大数据来测试人类行为理论。 该项目由人类网络和数据科学-基础设施（HNDS-I）和刺激竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The COVID-19 Online Prevalence of Emotions in Institutions (COPE-ID) database contains online discussions of COVID-19, including posts about emotions, such as fear, anxiety, and social institutions, such as healthcare and family. These data can be used to answer questions about the spread of information and individual well-being during the COVID-19 pandemic. This project creates a data visualization tool to process social media data from COPE-ID. This tool makes it easier for people to explore large volumes of social media data to study the emotions, thoughts, behaviors, and health of people during a pandemic or related disaster. The visualization tool allows researchers of all backgrounds and skill levels to access and process data from the COPE-ID, as well as data from other social media sources. Improving access to COPE-ID data can inform future public health policies and interventions. The data visualization tool’s users will be able to access an overview of large social media datasets through a platform dashboard. The dashboard presents visualizations of the data that are constructed by topic modeling algorithms, which produce a summary of the data in the form of word and topic frequencies. The tool also allows users to perform sentiment analysis, such as the attitude toward topics from negative to positive. Visualizations such as word clouds and time series charts generate insights for users to drive their task-based interactions with the tool. Users can also request samples of data that can be labeled using qualitative or content analysis. This labeled data can then be used to make predictions about future events, predictions that are generated by advanced statistical analyses or machine learning techniques. Training datasets can be used to code and process COPE-ID data, and these coded datasets can be used to examine the rate of agreement between coders so that the quality of the data can be improved. The tool improves scientists' access to social media data and allow researchers to test theories of human behavior using user generated big data. This project is jointly funded by Human Networks and Data Science -- Infrastructure (HNDS-I) and the Established Program to Stimulate Competitive Research (EPSCoR).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.