↓ Skip to main content

Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review

Overview of attention for article published in BMC Medical Informatics and Decision Making, June 2018
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (70th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
10 tweeters

Citations

dimensions_citation
45 Dimensions

Readers on

mendeley
114 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review
Published in
BMC Medical Informatics and Decision Making, June 2018
DOI 10.1186/s12911-018-0621-y
Pubmed ID
Authors

Andrea C. Tricco, Wasifa Zarin, Erin Lillie, Serena Jeblee, Rachel Warren, Paul A. Khan, Reid Robson, Ba’ Pham, Graeme Hirst, Sharon E. Straus

Abstract

A scoping review to characterize the literature on the use of conversations in social media as a potential source of data for detecting adverse events (AEs) related to health products. Our specific research questions were (1) What social media listening platforms exist to detect adverse events related to health products, and what are their capabilities and characteristics? (2) What is the validity and reliability of data from social media for detecting these adverse events? MEDLINE, EMBASE, Cochrane Library, and relevant websites were searched from inception to May 2016. Any type of document (e.g., manuscripts, reports) that described the use of social media data for detecting health product AEs was included. Two reviewers independently screened citations and full-texts, and one reviewer and one verifier performed data abstraction. Descriptive synthesis was conducted. After screening 3631 citations and 321 full-texts, 70 unique documents with 7 companion reports available from 2001 to 2016 were included. Forty-six documents (66%) described an automated or semi-automated information extraction system to detect health product AEs from social media conversations (in the developmental phase). Seven pre-existing information extraction systems to mine social media data were identified in eight documents. Nineteen documents compared AEs reported in social media data with validated data and found consistent AE discovery in all except two documents. None of the documents reported the validity and reliability of the overall system, but some reported on the performance of individual steps in processing the data. The validity and reliability results were found for the following steps in the data processing pipeline: data de-identification (n = 1), concept identification (n = 3), concept normalization (n = 2), and relation extraction (n = 8). The methods varied widely, and some approaches yielded better results than others. Our results suggest that the use of social media conversations for pharmacovigilance is in its infancy. Although social media data has the potential to supplement data from regulatory agency databases; is able to capture less frequently reported AEs; and can identify AEs earlier than official alerts or regulatory changes, the utility and validity of the data source remains under-studied. Open Science Framework ( https://osf.io/kv9hu/ ).

Twitter Demographics

The data shown below were collected from the profiles of 10 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 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 114 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 19 17%
Student > Ph. D. Student 15 13%
Other 8 7%
Researcher 8 7%
Student > Doctoral Student 8 7%
Other 19 17%
Unknown 37 32%
Readers by discipline Count As %
Computer Science 18 16%
Medicine and Dentistry 11 10%
Pharmacology, Toxicology and Pharmaceutical Science 9 8%
Nursing and Health Professions 7 6%
Business, Management and Accounting 5 4%
Other 18 16%
Unknown 46 40%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 06 December 2020.
All research outputs
#4,820,338
of 20,029,941 outputs
Outputs from BMC Medical Informatics and Decision Making
#432
of 1,771 outputs
Outputs of similar age
#85,719
of 294,893 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#2
of 9 outputs
Altmetric has tracked 20,029,941 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,771 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 75% 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 294,893 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 70% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.