Evidence map›Paper›PMID 39240684›Full record

ArticleJMIR public health and surveillance2024

The Value of Social Media Analysis for Adverse Events Detection and Pharmacovigilance: Scoping Review.

Su Golder, Karen O'Connor, Yunwen Wang, Ari Klein, Graciela Gonzalez Hernandez

Abstract readScoping Review
In one paragraph

Article in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Observational
  2. Article
  3. Observational
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  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Su GolderUniversity of York, York, United Kingdom.ORCID 0000-0002-8987-5211
Karen O'ConnorUniversity of Pennsylvannia, Philadelphia, PA, United States.ORCID 0000-0001-7709-3813
Yunwen WangCedars-Sinai Medical Center, Los Angeles, CA, United States.ORCID 0000-0002-3197-1366
Ari KleinUniversity of Pennsylvannia, Philadelphia, PA, United States.ORCID 0000-0002-8281-3464
Graciela Gonzalez HernandezCedars-Sinai Medical Center, Los Angeles, CA, United States.ORCID 0000-0002-6416-9556

Funding

Social Media Mining for PharmacovigilanceR01LM011176 · NLM · UNIVERSITY OF PENNSYLVANIA · PI GONZALEZ HERNANDEZ, GRACIELA, SHEN, LI · 2012 to 2021
$4.5M
NLM NIH HHS R01 LM011176
6 · The paper itself

Abstract

backgroundAdverse drug events pose an enormous public health burden, leading to hospitalization, disability, and death. Even the adverse events (AEs) categorized as nonserious can severely impact on patient's quality of life, adherence, and persistence. Monitoring medication safety is challenging. Web-based patient reports on social media may be a useful supplementary source of real-world data. Despite the growth of sophisticated techniques for identifying AEs using social media data, a consensus has not been reached as to the value of social media in relation to more traditional data sources.

objectiveThis study aims to evaluate and characterize the utility of social media analysis in adverse drug event detection and pharmacovigilance as compared with other data sources (such as spontaneous reporting systems and the clinical literature).

methodsIn this scoping review, we searched 11 bibliographical databases and Google Scholar, followed by handsearching and forward and backward citation searching. Each record was screened by 2 independent reviewers at both the title and abstract stage and the full-text screening stage. Studies were included if they used any type of social media (such as Twitter or patient forums) to detect AEs associated with any drug medication and compared the results ascertained from social media to any other data source. Study information was collated using a piloted data extraction sheet. Data were extracted on the AEs and drugs searched for and included; the methods used (such as machine learning); social media data source; volume of data analyzed; limitations of the methodology; availability of data and code; comparison data source and comparison methods; results, including the volume of AEs, and how the AEs found compared with other data sources in their seriousness, frequencies, and expectedness or novelty (new vs known knowledge); and conclusions.

resultsOf the 6538 unique records screened, 73 publications representing 60 studies with a wide variety of extraction methods met our inclusion criteria. The most common social media platforms used were Twitter and online health forums. The most common comparator data source was spontaneous reporting systems, although other comparisons were also made, such as with scientific literature and product labels. Although similar patterns of AE reporting tended to be identified, the frequencies were lower in social media. Social media data were found to be useful in identifying new or unexpected AEs and in identifying AEs in a timelier manner.

conclusionsThere is a large body of research comparing AEs from social media to other sources. Most studies advocate the use of social media as an adjunct to traditional data sources. Some studies also indicate the value of social media in understanding patient perspectives such as the impact of AEs, which could be better explored. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/47068.

Indexed as

Drug-Related Side Effects and Adverse ReactionsPharmacovigilanceSocial MediaAdverse Drug Reaction Reporting SystemsHumansadverse eventspharmacovigilancereal-world datascoping reviewsocial media

Identifiers

PMID39240684
PMCPMC11415724

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.