# Divergent modes of online collective attention to the COVID-19 pandemic are associated with future caseload variance

arXiv: https://arxiv.org/abs/2004.03516 · HTML: https://mlsanity.com/paper/2004.03516/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Learning from pandemics: using extraordinary events can improve disease now-casting models](https://mlsanity.com/paper/2101.06774/) (2021)

## Abstract (arXiv)

Using a random 10% sample of tweets authored from 2019-09-01 through 2020-04-30, we analyze the dynamic behavior of words (1-grams) used on Twitter to describe the ongoing COVID-19 pandemic. Across 24 languages, we find two distinct dynamic regimes: One characterizing the rise and subsequent collapse in collective attention to the initial Coronavirus outbreak in late January, and a second that represents March COVID-19-related discourse. Aggregating countries by dominant language use, we find that volatility in the first dynamic regime is associated with future volatility in new cases of COVID-19 roughly three weeks (average 22.49 ± 3.26 days) later. Our results suggest that surveillance of change in usage of epidemiology-related words on social media may be useful in forecasting later change in disease case numbers, but we emphasize that our current findings are not causal or necessarily predictive.