The COVID-19 pandemic is having a dramatic impact on societies and economies around the world. With various measures of lockdowns and social distancing in place, it becomes important to understand emotional responses on a large scale. In this paper, we present the first ground truth dataset of emotional responses to COVID-19. We asked participants to indicate their emotions and express these in text and created the Real World Worry Dataset of 5,000 texts (2,500 short + 2,500 long texts). Our analyses suggest that emotional responses correlated with linguistic measures. Topic modeling further revealed that people in the UK worry about their family and the economic situation. Tweet-sized texts functioned as a call for solidarity, while longer texts shed light on worries and concerns. Using predictive modeling approaches, we were able to approximate the emotional responses of participants from text within 14\% of their actual value. We encourage others to use the dataset and improve how we can use automated methods to learn about emotional responses and worries about an urgent problem.

本研究提供了第一个 COVID-19 情感反应的真实数据集，并分析了语言模式与情感响应的相关性，其中英国人担心家庭和经济状况，短文本呼吁团结，而长文本更详细地阐述了担忧和关切。通过预测建模方法，我们能够在14％的准确率内预测参与者的情感反应，鼓励其他人使用此数据集并改进自动化方法以了解紧迫问题的情感反应和担忧。

COVID-19真实世界担忧数据集中的情绪测量