↓ Skip to main content

Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online

Overview of attention for article published in arXiv, May 2021
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#9 of 945,844)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
26 news outlets
blogs
4 blogs
twitter
9267 X users
facebook
1 Facebook page
reddit
7 Redditors

Citations

dimensions_citation
94 Dimensions

Readers on

mendeley
168 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online
Published in
arXiv, May 2021
DOI 10.1145/3411764.3445211
Authors

Crystal Lee, Tanya Yang, Gabrielle D Inchoco, Graham M. Jones, Arvind Satyanarayan

X Demographics

X Demographics

The data shown below were collected from the profiles of 9,267 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 168 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 168 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 22%
Researcher 26 15%
Student > Master 17 10%
Other 6 4%
Student > Doctoral Student 6 4%
Other 27 16%
Unknown 49 29%
Readers by discipline Count As %
Computer Science 32 19%
Social Sciences 20 12%
Medicine and Dentistry 9 5%
Business, Management and Accounting 7 4%
Design 6 4%
Other 38 23%
Unknown 56 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4564. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 April 2024.
All research outputs
#961
of 25,795,662 outputs
Outputs from arXiv
#9
of 945,844 outputs
Outputs of similar age
#68
of 456,583 outputs
Outputs of similar age from arXiv
#1
of 26,935 outputs
Altmetric has tracked 25,795,662 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 945,844 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 456,583 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 26,935 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.