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Is searching full text more effective than searching abstracts?

Overview of attention for article published in BMC Bioinformatics, February 2009
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

twitter
13 tweeters

Citations

dimensions_citation
38 Dimensions

Readers on

mendeley
142 Mendeley
citeulike
25 CiteULike
connotea
7 Connotea
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Title
Is searching full text more effective than searching abstracts?
Published in
BMC Bioinformatics, February 2009
DOI 10.1186/1471-2105-10-46
Pubmed ID
Authors

Jimmy Lin

Abstract

With the growing availability of full-text articles online, scientists and other consumers of the life sciences literature now have the ability to go beyond searching bibliographic records (title, abstract, metadata) to directly access full-text content. Motivated by this emerging trend, I posed the following question: is searching full text more effective than searching abstracts? This question is answered by comparing text retrieval algorithms on MEDLINE abstracts, full-text articles, and spans (paragraphs) within full-text articles using data from the TREC 2007 genomics track evaluation. Two retrieval models are examined: bm25 and the ranking algorithm implemented in the open-source Lucene search engine.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
United Kingdom 4 3%
India 1 <1%
Brazil 1 <1%
Sweden 1 <1%
Korea, Republic of 1 <1%
Netherlands 1 <1%
Mexico 1 <1%
Denmark 1 <1%
Other 2 1%
Unknown 125 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 22%
Student > Ph. D. Student 27 19%
Student > Master 25 18%
Other 10 7%
Professor 9 6%
Other 30 21%
Unknown 10 7%
Readers by discipline Count As %
Computer Science 55 39%
Agricultural and Biological Sciences 25 18%
Social Sciences 14 10%
Medicine and Dentistry 11 8%
Business, Management and Accounting 5 4%
Other 17 12%
Unknown 15 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 March 2020.
All research outputs
#2,172,934
of 14,714,661 outputs
Outputs from BMC Bioinformatics
#882
of 5,475 outputs
Outputs of similar age
#22,254
of 148,906 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 25 outputs
Altmetric has tracked 14,714,661 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,475 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 83% 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 148,906 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.