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COVID-related tweets in the period between January and May 2020


Castioni, Piergiorgio (2022), COVID-related tweets in the period between January and May 2020, Dryad, Dataset,


Online platforms play a relevant role in the creation and diffusion of false or misleading news. Concerningly, the COVID-19 pandemic is shaping a communication network which reflects the emergence of collective attention towards a topic that rapidly gained universal interest. Here, we characterize the dynamics of this network on Twitter, analysing how unreliable content distributes among its users. We find that a minority of accounts is responsible for the majority of the misinformation circulating online, and identify two categories of users: a few active ones, playing the role of ‘creators’, and a majority playing the role of ‘consumers’. The relative proportion of these groups (approx. 14% creators—86% consumers) appears stable over time: consumers are mostly exposed to the opinions of a vocal minority of creators (which are the origin of 82% of fake content in our data), that could be mistakenly understood as representative of the majority of users. The corresponding pressure from a perceived majority is identified as a potential driver of the ongoing COVID-19 infodemic.


The datasets that we used in this work come from the COVID-19 Infodemics Observatory ( Tweets associated with the COVID-19 pandemics (coronavirus, ncov, #Wuhan, covid19, COVID-19, SARSCoV2, COVID) have been automatically collected using the Twitter Filter API.

It contains 7.7 million retweets in the case of USA, 300 thousand in the case of Italy and 900 thousand in the case of the UK.

The time of the collection goes from the 22nd of January to the 22nd of May for the USA, while for Italy and the UK it goes from the 22nd of January to the 2nd of December.

For each tweet we specified the ID code as well as the time at which it was created.

In this dataset one can also find the tables necessary to reproduce exactly the figures in the paper.