ArticleBMC medical informatics and decision making2018
Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review.
Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ethical and Methodological Considerations of Twitter Data for Public Health Research: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Attitudes toward pig islet xenotransplantation for type 1 diabetes: a scoping review.Journal of endocrinological investigation · 2025Article
- Dermatology in Student-Run Clinics in the United States: Scoping Review.JMIR dermatology · 2024Article
- The Value of Social Media Analysis for Adverse Events Detection and Pharmacovigilance: Scoping Review.JMIR public health and surveillance · 2024Article
- Social Media as Pharmacovigilance: The Potential for Patient Reports to Inform Clinical Research on Glucagon-Like Peptide 1 (GLP-1) Receptor Agonists for Substance Use Disorders.Journal of studies on alcohol and drugs · 2024Article
- Artificial intelligence-enabled social media listening to inform early patient-focused drug development: perspectives on approaches and strategies.Frontiers in digital health · 2024Article
- Leveraging patient experience data to guide medicines development, regulation, access decisions and clinical care in the EU.Frontiers in medicine · 2024Review
- Using natural language processing to characterize and predict homeopathic product-associated adverse events in consumer reviews: comparison to reports to FDA Adverse Event Reporting System (FAERS).Journal of the American Medical Informatics Association : JAMIA · 2023Article
- GLP-1 Receptor Agonists and Related Mental Health Issues; Insights from a Range of Social Media Platforms Using a Mixed-Methods Approach.Brain sciences · 2023Article
- Estimating Patient Satisfaction Through a Language Processing Model: Model Development and Evaluation.JMIR formative research · 2023Article
- Strategies and interventions to strengthen pharmacovigilance systems in low-income and middle-income countries: a scoping review.BMJ open · 2023Article
- The Role of Social Media for Identifying Adverse Drug Events Data in Pharmacovigilance: Protocol for a Scoping Review.JMIR research protocols · 2023Article
- Using GPT-3 to Build a Lexicon of Drugs of Abuse Synonyms for Social Media Pharmacovigilance.Biomolecules · 2023Article
- Barriers to opioid use disorder treatment: A comparison of self-reported information from social media with barriers found in literature.Frontiers in public health · 2023Article
- Social media and internet search data to inform drug utilization: A systematic scoping review.Frontiers in digital health · 2023Article
- Patient reporting of sexual adverse events on an online platform for medication experiences.British journal of clinical pharmacology · 2022Article
- Spontaneously generated online patient experience data - how and why is it being used in health research: an umbrella scoping review.BMC medical research methodology · 2022Article
- Knowledge-based approaches to drug discovery for rare diseases.Drug discovery today · 2022Review
- Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter Data.Vaccines · 2022Article
- Social Media Analytics for Pharmacovigilance of Antiepileptic Drugs.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundA 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.
methodsOur 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.
resultsAfter 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.
conclusionsOur 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 registrationOpen Science Framework ( https://osf.io/kv9hu/ ).
Indexed as
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What OpenQuestion holds
Registered trials
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.