Related papers: Measuring Emotions in the COVID-19 Real World Worry Dataset

Measuring Emotions in the COVID-19 Real World Worry Dataset

URL: http://arxiv.org/abs/2004.04225v2
Date: Thu, 14 May 2020 17:57:23 GMT
Title: Measuring Emotions in the COVID-19 Real World Worry Dataset
Authors: Bennett Kleinberg, Isabelle van der Vegt, Maximilian Mozes
Abstract summary: This paper presents the first ground truth dataset of emotional responses to COVID-19. We asked participants to indicate their emotions and express these in text. Our analyses suggest that emotional responses correlated with linguistic measures.
Score: 0.9410583483182656
License: http://creativecommons.org/licenses/by/4.0/
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.

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