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Detecting tax evasion: a co-evolutionary approach

Overview of attention for article published in Artificial Intelligence and Law, April 2016
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About this Attention Score

  • Among the highest-scoring outputs from this source (#37 of 214)
  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

policy
1 policy source
twitter
3 X users

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
63 Mendeley
Title
Detecting tax evasion: a co-evolutionary approach
Published in
Artificial Intelligence and Law, April 2016
DOI 10.1007/s10506-016-9181-6
Authors

Erik Hemberg, Jacob Rosen, Geoff Warner, Sanith Wijesinghe, Una-May O’Reilly

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 63 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 19%
Student > Ph. D. Student 8 13%
Researcher 7 11%
Lecturer 5 8%
Student > Bachelor 5 8%
Other 6 10%
Unknown 20 32%
Readers by discipline Count As %
Computer Science 11 17%
Business, Management and Accounting 9 14%
Economics, Econometrics and Finance 9 14%
Social Sciences 7 11%
Engineering 3 5%
Other 3 5%
Unknown 21 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 10 March 2023.
All research outputs
#6,326,717
of 23,849,058 outputs
Outputs from Artificial Intelligence and Law
#37
of 214 outputs
Outputs of similar age
#86,399
of 301,237 outputs
Outputs of similar age from Artificial Intelligence and Law
#3
of 5 outputs
Altmetric has tracked 23,849,058 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 214 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one has gotten more attention than average, scoring higher than 65% 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 301,237 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.