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University of Cambridge

India nudges to contain COVID-19 pandemic: A reactive public policy analysis using machine-learning based topic modelling

Overview of attention for article published in PLOS ONE, September 2020
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
25 news outlets
blogs
2 blogs
twitter
115 X users
facebook
1 Facebook page

Citations

dimensions_citation
66 Dimensions

Readers on

mendeley
371 Mendeley
Title
India nudges to contain COVID-19 pandemic: A reactive public policy analysis using machine-learning based topic modelling
Published in
PLOS ONE, September 2020
DOI 10.1371/journal.pone.0238972
Pubmed ID
Authors

Ramit Debnath, Ronita Bardhan

X Demographics

X Demographics

The data shown below were collected from the profiles of 115 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 371 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 371 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 33 9%
Researcher 32 9%
Student > Ph. D. Student 29 8%
Lecturer 27 7%
Other 20 5%
Other 75 20%
Unknown 155 42%
Readers by discipline Count As %
Medicine and Dentistry 34 9%
Social Sciences 34 9%
Computer Science 20 5%
Business, Management and Accounting 18 5%
Economics, Econometrics and Finance 12 3%
Other 81 22%
Unknown 172 46%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 296. 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 17 September 2022.
All research outputs
#115,850
of 25,121,016 outputs
Outputs from PLOS ONE
#1,810
of 217,939 outputs
Outputs of similar age
#3,605
of 408,118 outputs
Outputs of similar age from PLOS ONE
#36
of 2,857 outputs
Altmetric has tracked 25,121,016 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 217,939 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.7. 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 408,118 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 2,857 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 98% of its contemporaries.