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Discovering opinion leaders for medical topics using news articles

Overview of attention for article published in Journal of Biomedical Semantics, March 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#7 of 368)
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

blogs
2 blogs
twitter
15 X users

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
63 Mendeley
citeulike
4 CiteULike
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Title
Discovering opinion leaders for medical topics using news articles
Published in
Journal of Biomedical Semantics, March 2012
DOI 10.1186/2041-1480-3-2
Pubmed ID
Authors

Siddhartha Jonnalagadda, Ryan Peeler, Philip Topham

Abstract

Rapid identification of subject experts for medical topics helps in improving the implementation of discoveries by speeding the time to market drugs and aiding in clinical trial recruitment, etc. Identifying such people who influence opinion through social network analysis is gaining prominence. In this work, we explore how to combine named entity recognition from unstructured news articles with social network analysis to discover opinion leaders for a given medical topic.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 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 %
United States 3 5%
Korea, Republic of 1 2%
Switzerland 1 2%
Brazil 1 2%
Australia 1 2%
Unknown 56 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 21%
Student > Ph. D. Student 9 14%
Other 6 10%
Student > Master 6 10%
Student > Bachelor 4 6%
Other 15 24%
Unknown 10 16%
Readers by discipline Count As %
Computer Science 17 27%
Social Sciences 10 16%
Agricultural and Biological Sciences 7 11%
Medicine and Dentistry 6 10%
Business, Management and Accounting 3 5%
Other 11 17%
Unknown 9 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 25 May 2012.
All research outputs
#1,667,898
of 25,371,288 outputs
Outputs from Journal of Biomedical Semantics
#7
of 368 outputs
Outputs of similar age
#9,007
of 169,059 outputs
Outputs of similar age from Journal of Biomedical Semantics
#1
of 8 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 368 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 98% 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 169,059 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 94% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them