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. 2018 Jun 14;18(1):38.
doi: 10.1186/s12911-018-0621-y.

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

Andrea C Tricco  1   2 , Wasifa Zarin  3 , Erin Lillie  3 , Serena Jeblee  4 , Rachel Warren  3 , Paul A Khan  3 , Reid Robson  3 , Ba' Pham  3 , Graeme Hirst  4 , Sharon E Straus  3   5
Affiliations

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

Andrea C Tricco et al. BMC Med Inform Decis Mak. .

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Trial registration: Open Science Framework ( https://osf.io/kv9hu/ ).

Keywords: Adverse event; Data analytics; Drug safety; Knowledge synthesis; Social media; Surveillance.

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Conflict of interest statement

Ethics approval and consent to participate

Since this is a scoping review, ethics approval was not required.

Competing interests

The authors declare that they have no competing interests. The study funder had no input into the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

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Figures

Fig. 1
Fig. 1
Study flow diagram
Fig. 2
Fig. 2
Wordcloud of social media sources mined in the documents
Fig. 3
Fig. 3
Steps typically involved in social media data processing flow
See this image and copyright information in PMC

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