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Disease mentions in airport and hospital geolocations expose dominance of news events for disease concerns

Overview of attention for article published in Journal of Biomedical Semantics, June 2018
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)

Mentioned by

twitter
5 X users

Citations

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8 Dimensions

Readers on

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32 Mendeley
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Title
Disease mentions in airport and hospital geolocations expose dominance of news events for disease concerns
Published in
Journal of Biomedical Semantics, June 2018
DOI 10.1186/s13326-018-0186-9
Pubmed ID
Authors

Joana M. Barros, Jim Duggan, Dietrich Rebholz-Schuhmann

Abstract

In recent years, Twitter has been applied to monitor diseases through its facility to monitor users' comments and concerns in real-time. The analysis of tweets for disease mentions should reflect not only user specific concerns but also disease outbreaks. This requires the use of standard terminological resources and can be focused on selected geographic locations. In our study, we differentiate between hospital and airport locations to better distinguish disease outbreaks from background mentions of disease concerns. Our analysis covers all geolocated tweets over a 6 months time period, uses SNOMED-CT as a standard medical terminology, and explores language patterns (as well as MetaMap) to identify mentions of diseases in reference to the geolocation of tweets. Contrary to our expectation, hospital and airport geolocations are not suitable to collect significant portions of tweets concerned with disease outcomes. Overall, geolocated tweets exposed a large number of messages commenting on disease-related news articles. Furthermore, the geolocated messages exposed an over-representation of non-communicable diseases in contrast to infectious diseases. Our findings suggest that disease mentions on Twitter not only serve the purpose to share personal statements but also to share concerns about news articles. In particular, our assumption about the relevance of hospital and airport geolocations for an increased frequency of diseases mentions has not been met. To further address the linguistic cues, we propose the study of health forums to understand how a change in medium affects the language applied by the users. Finally, our research on the language use may provide essential clues to distinguish complementary trends in the use of language in Twitter when analysing health-related topics.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 19%
Student > Master 4 13%
Researcher 3 9%
Student > Bachelor 3 9%
Student > Doctoral Student 2 6%
Other 4 13%
Unknown 10 31%
Readers by discipline Count As %
Medicine and Dentistry 6 19%
Business, Management and Accounting 3 9%
Computer Science 3 9%
Social Sciences 2 6%
Nursing and Health Professions 1 3%
Other 4 13%
Unknown 13 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 01 August 2019.
All research outputs
#12,784,465
of 23,090,520 outputs
Outputs from Journal of Biomedical Semantics
#170
of 366 outputs
Outputs of similar age
#151,763
of 328,349 outputs
Outputs of similar age from Journal of Biomedical Semantics
#1
of 4 outputs
Altmetric has tracked 23,090,520 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 366 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 53% 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 328,349 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 4 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