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Data science: developing theoretical contributions in information systems via text analytics

Overview of attention for article published in Journal of Big Data, January 2020
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 X user

Citations

dimensions_citation
29 Dimensions

Readers on

mendeley
171 Mendeley
Title
Data science: developing theoretical contributions in information systems via text analytics
Published in
Journal of Big Data, January 2020
DOI 10.1186/s40537-019-0280-6
Authors

Aya Rizk, Ahmed Elragal

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 171 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 22 13%
Student > Ph. D. Student 17 10%
Student > Bachelor 15 9%
Lecturer 13 8%
Student > Doctoral Student 11 6%
Other 21 12%
Unknown 72 42%
Readers by discipline Count As %
Computer Science 37 22%
Business, Management and Accounting 20 12%
Engineering 12 7%
Social Sciences 7 4%
Agricultural and Biological Sciences 2 1%
Other 16 9%
Unknown 77 45%
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 13 June 2021.
All research outputs
#15,685,238
of 23,308,124 outputs
Outputs from Journal of Big Data
#208
of 351 outputs
Outputs of similar age
#275,640
of 457,615 outputs
Outputs of similar age from Journal of Big Data
#7
of 14 outputs
Altmetric has tracked 23,308,124 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 351 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.4. This one is in the 25th percentile – i.e., 25% of its peers scored the same or lower than it.
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 457,615 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 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 50% of its contemporaries.