# A repeated-measures study on emotional responses after a year in the pandemic

arXiv: https://arxiv.org/abs/2107.03466 · HTML: https://mlsanity.com/paper/2107.03466/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 45 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [An Exploratory Study of COVID-19 Misinformation on Twitter](https://mlsanity.com/paper/2005.05710/) (2020)
- [Measuring Emotions in the COVID-19 Real World Worry Dataset](https://mlsanity.com/paper/2004.04225/) (2020)
- [#lockdown: network-enhanced emotional profiling at the times of COVID-19](https://mlsanity.com/paper/2005.04404/) (2020)
- [Recovering the number of clusters in data sets with noise features using feature rescaling factors](https://mlsanity.com/paper/1602.06989/) (2016)
- [Quantifying the Effects of COVID-19 on Mental Health Support Forums](https://mlsanity.com/paper/2009.04008/) (2020)
- [Women worry about family, men about the economy: Gender differences in emotional responses to COVID-19](https://mlsanity.com/paper/2004.08202/) (2020)
- [How Epidemic Psychology Works on Twitter: Evolution of responses to the COVID-19 pandemic in the U.S](https://mlsanity.com/paper/2007.13169/) (2020)

## Built on by (most cited first, 3 of 3)

- [The RW3D: A multi-modal panel dataset to understand the psychological impact of the pandemic](https://mlsanity.com/paper/2302.00606/) (2023)
- [When lies are mostly truthful: automated verbal deception detection for embedded lies](https://mlsanity.com/paper/2501.07217/) (2025)
- [Linguistic traces of stochastic empathy in language models](https://mlsanity.com/paper/2410.01675/) (2024)

## Abstract (arXiv)

The introduction of COVID-19 lockdown measures and an outlook on return to normality are demanding societal changes. Among the most pressing questions is how individuals adjust to the pandemic. This paper examines the emotional responses to the pandemic in a repeated-measures design. Data (n=1698) were collected in April 2020 (during strict lockdown measures) and in April 2021 (when vaccination programmes gained traction). We asked participants to report their emotions and express these in text data. Statistical tests revealed an average trend towards better adjustment to the pandemic. However, clustering analyses suggested a more complex heterogeneous pattern with a well-coping and a resigning subgroup of participants. Linguistic computational analyses uncovered that topics and n-gram frequencies shifted towards attention to the vaccination programme and away from general worrying. Implications for public mental health efforts in identifying people at heightened risk are discussed. The dataset is made publicly available.