# Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time

arXiv: https://arxiv.org/abs/2103.08952 · HTML: https://mlsanity.com/paper/2103.08952/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 60 references that are in the map.
- First posted on arXiv 16 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter](https://mlsanity.com/paper/2004.06986/) (2020)
- [What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing](https://mlsanity.com/paper/2004.10899/) (2020)
- [Understanding Citizen Reactions and Ebola-Related Information Propagation on Social Media](https://mlsanity.com/paper/1607.04318/) (2016)
- [Risk Communication in Asian Countries: COVID-19 Discourse on Twitter](https://mlsanity.com/paper/2006.12218/) (2020)

## Built on by (most cited first, 5 of 5)

- [An Analysis of COVID-19 Knowledge Graph Construction and Applications](https://mlsanity.com/paper/2110.04932/) (2021)
- [How do media talk about the Covid-19 pandemic? Metaphorical thematic clustering in Italian online newspapers](https://mlsanity.com/paper/2204.02106/) (2022)
- [A Large-Scale Analysis of Persian Tweets Regarding Covid-19 Vaccination](https://mlsanity.com/paper/2302.04511/) (2023)
- [Leveraging Natural Language Processing For Public Health Screening On YouTube: A COVID-19 Case Study](https://mlsanity.com/paper/2306.01164/) (2023)
- [Exploring Topic Trends in COVID-19 Research Literature using Non-Negative Matrix Factorization](https://mlsanity.com/paper/2503.18182/) (2025)

## Abstract (arXiv)

The words we use to talk about the current epidemiological crisis on social media can inform us on how we are conceptualizing the pandemic and how we are reacting to its development. This paper provides an extensive explorative analysis of how the discourse about Covid-19 reported on Twitter changes through time, focusing on the first wave of this pandemic. Based on an extensive corpus of tweets (produced between 20th March and 1st July 2020) first we show how the topics associated with the development of the pandemic changed through time, using topic modeling. Second, we show how the sentiment polarity of the language used in the tweets changed from a relatively positive valence during the first lockdown, toward a more negative valence in correspondence with the reopening. Third we show how the average subjectivity of the tweets increased linearly and fourth, how the popular and frequently used figurative frame of WAR changed when real riots and fights entered the discourse.