# Measuring Emotions in the COVID-19 Real World Worry Dataset

arXiv: https://arxiv.org/abs/2004.04225 · HTML: https://mlsanity.com/paper/2004.04225/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2020.

## Built on by (most cited first, 15 of 15)

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- [Choosing the Number of Topics in LDA Models -- A Monte Carlo Comparison of Selection Criteria](https://mlsanity.com/paper/2212.14074/) (2022)
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- [Applications and Challenges of Sentiment Analysis in Real-life Scenarios](https://mlsanity.com/paper/2301.09912/) (2023)
- [The RW3D: A multi-modal panel dataset to understand the psychological impact of the pandemic](https://mlsanity.com/paper/2302.00606/) (2023)
- [Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets](https://mlsanity.com/paper/2302.14727/) (2023)
- [Topological Interpretations of GPT-3](https://mlsanity.com/paper/2308.03565/) (2023)
- [Data Augmentation for Emotion Detection in Small Imbalanced Text Data](https://mlsanity.com/paper/2310.17015/) (2023)
- [A Tutorial on the Pretrain-Finetune Paradigm for Natural Language Processing](https://mlsanity.com/paper/2403.02504/) (2024)
- [SenWave: A Fine-Grained Multi-Language Sentiment Analysis Dataset Sourced from COVID-19 Tweets](https://mlsanity.com/paper/2510.08214/) (2025)
- [LEXpander: applying colexification networks to automated lexicon expansion](https://mlsanity.com/paper/2205.15850/) (2022)

## Abstract (arXiv)

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.