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X Demographics
Mendeley readers
Attention Score in Context
| Title |
What's unusual in online disease outbreak news?
|
|---|---|
| Published in |
Journal of Biomedical Semantics, March 2010
|
| DOI | 10.1186/2041-1480-1-2 |
| Pubmed ID | |
| Authors |
Nigel Collier |
| Abstract |
Accurate and timely detection of public health events of international concern is necessary to help support risk assessment and response and save lives. Novel event-based methods that use the World Wide Web as a signal source offer potential to extend health surveillance into areas where traditional indicator networks are lacking. In this paper we address the issue of systematically evaluating online health news to support automatic alerting using daily disease-country counts text mined from real world data using BioCaster. For 18 data sets produced by BioCaster, we compare 5 aberration detection algorithms (EARS C2, C3, W2, F-statistic and EWMA) for performance against expert moderated ProMED-mail postings. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 6% |
| Colombia | 1 | 1% |
| Italy | 1 | 1% |
| Portugal | 1 | 1% |
| United Kingdom | 1 | 1% |
| Sweden | 1 | 1% |
| Spain | 1 | 1% |
| Canada | 1 | 1% |
| Unknown | 57 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 26% |
| Student > Ph. D. Student | 13 | 19% |
| Student > Master | 12 | 18% |
| Other | 7 | 10% |
| Student > Bachelor | 5 | 7% |
| Other | 6 | 9% |
| Unknown | 7 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 20 | 29% |
| Medicine and Dentistry | 14 | 21% |
| Agricultural and Biological Sciences | 6 | 9% |
| Social Sciences | 5 | 7% |
| Nursing and Health Professions | 4 | 6% |
| Other | 9 | 13% |
| Unknown | 10 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 17 October 2011.
All research outputs
#3,779,497
of 25,374,917 outputs
Outputs from Journal of Biomedical Semantics
#58
of 368 outputs
Outputs of similar age
#14,570
of 103,451 outputs
Outputs of similar age from Journal of Biomedical Semantics
#2
of 3 outputs
Altmetric has tracked 25,374,917 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 368 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 84% 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 103,451 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 85% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.