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Using text mining for study identification in systematic reviews: a systematic review of current approaches

Overview of attention for article published in Systematic Reviews, January 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#14 of 1,858)
  • 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
1 news outlet
blogs
1 blog
policy
2 policy sources
twitter
210 tweeters
patent
1 patent
weibo
1 weibo user
facebook
3 Facebook pages
reddit
1 Redditor

Citations

dimensions_citation
310 Dimensions

Readers on

mendeley
520 Mendeley
citeulike
5 CiteULike
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Title
Using text mining for study identification in systematic reviews: a systematic review of current approaches
Published in
Systematic Reviews, January 2015
DOI 10.1186/2046-4053-4-5
Pubmed ID
Authors

Alison O’Mara-Eves, James Thomas, John McNaught, Makoto Miwa, Sophia Ananiadou

Abstract

The large and growing number of published studies, and their increasing rate of publication, makes the task of identifying relevant studies in an unbiased way for inclusion in systematic reviews both complex and time consuming. Text mining has been offered as a potential solution: through automating some of the screening process, reviewer time can be saved. The evidence base around the use of text mining for screening has not yet been pulled together systematically; this systematic review fills that research gap. Focusing mainly on non-technical issues, the review aims to increase awareness of the potential of these technologies and promote further collaborative research between the computer science and systematic review communities.

Twitter Demographics

The data shown below were collected from the profiles of 210 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 520 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 1%
Brazil 2 <1%
United Kingdom 2 <1%
Sweden 1 <1%
Finland 1 <1%
Canada 1 <1%
Austria 1 <1%
Spain 1 <1%
Denmark 1 <1%
Other 0 0%
Unknown 503 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 95 18%
Student > Ph. D. Student 81 16%
Researcher 67 13%
Librarian 37 7%
Student > Bachelor 28 5%
Other 122 23%
Unknown 90 17%
Readers by discipline Count As %
Computer Science 119 23%
Medicine and Dentistry 92 18%
Agricultural and Biological Sciences 35 7%
Social Sciences 30 6%
Engineering 21 4%
Other 112 22%
Unknown 111 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 154. 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 May 2022.
All research outputs
#197,766
of 21,457,683 outputs
Outputs from Systematic Reviews
#14
of 1,858 outputs
Outputs of similar age
#2,777
of 344,321 outputs
Outputs of similar age from Systematic Reviews
#2
of 117 outputs
Altmetric has tracked 21,457,683 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 1,858 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.6. 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 344,321 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 117 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.