Measuring Emotions in the COVID-19 Real World Worry Dataset

TL;DR

This paper introduces a new dataset of 5,000 COVID-19 related texts capturing emotional responses, analyzes linguistic patterns, and demonstrates predictive modeling to estimate emotions from text.

Contribution

It provides the first ground truth dataset of COVID-19 emotional responses and shows how linguistic features relate to emotions and worries.

Findings

Emotional responses correlate with linguistic measures.

Topic modeling reveals specific worries like family and economy.

Predictive models estimate emotions within 14% accuracy.

Abstract

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. This resulted in 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.