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Detecting COVID-19-Related Fake News Using Feature Extraction

Overview of attention for article published in Frontiers in Public Health, January 2022
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

twitter
2 X users
facebook
1 Facebook page

Citations

dimensions_citation
34 Dimensions

Readers on

mendeley
108 Mendeley
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Title
Detecting COVID-19-Related Fake News Using Feature Extraction
Published in
Frontiers in Public Health, January 2022
DOI 10.3389/fpubh.2021.788074
Pubmed ID
Authors

Suleman Khan, Saqib Hakak, N. Deepa, B. Prabadevi, Kapal Dev, Silvia Trelova

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 108 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 108 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 6%
Unspecified 6 6%
Researcher 5 5%
Lecturer 5 5%
Student > Doctoral Student 4 4%
Other 14 13%
Unknown 67 62%
Readers by discipline Count As %
Computer Science 16 15%
Unspecified 6 6%
Social Sciences 4 4%
Medicine and Dentistry 4 4%
Business, Management and Accounting 2 2%
Other 5 5%
Unknown 71 66%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 20 May 2022.
All research outputs
#15,686,478
of 23,310,485 outputs
Outputs from Frontiers in Public Health
#4,857
of 10,839 outputs
Outputs of similar age
#284,249
of 508,482 outputs
Outputs of similar age from Frontiers in Public Health
#338
of 815 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,839 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.8. This one has gotten more attention than average, scoring higher than 51% 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 508,482 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 815 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.