ReviewTherapeutic advances in drug safety2018
Using social media in safety signal management: is it reliable?
Review in Therapeutic advances in drug safety, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed, 22 citations in OpenAlex.
- Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis.Journal of medical Internet research · 2025Article
- The Value of Social Media Analysis for Adverse Events Detection and Pharmacovigilance: Scoping Review.JMIR public health and surveillance · 2024Article
- Leveraging patient experience data to guide medicines development, regulation, access decisions and clinical care in the EU.Frontiers in medicine · 2024Review
- A qualitative content analysis of cannabis-related discussions on Reddit during the COVID-19 pandemic.PloS one · 2024Article
- BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.Frontiers in public health · 2024Article
- Integrating electronic health records with other data sources for postmarket drug safety signal identification: a review.Frontiers in drug safety and regulation · 2024Review
- The Use of Social Media in Detecting Drug Safety-Related New Black Box Warnings, Labeling Changes, or Withdrawals: Scoping Review.JMIR public health and surveillance · 2021Article
- [Utilizing social media data in post-market safety surveillance].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2021Article
- Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med Project.Drug safety · 2020Article
- Social media monitoring on the perceived safety of medication use during pregnancy: A case study from the Netherlands.British journal of clinical pharmacology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Social media use is growing globally, with a reported 3 billion active users in 2017. This medium is used increasingly in a health setting by patients (and to a limited extent, healthcare professionals) to share experiences and ask advice on medical conditions as well as pharmaceutical products. In recent years, attention has turned to this huge, generally untapped, source of potential health information as a possible tool for pharmacovigilance, and in particular signal detection. In this article we explore some of the challenges of utilizing social media for safety signal detection and look at some of the pilot studies conducted to date in order to weigh the evidence for and against the utility of social media data in safety signal detection. After doing so we can conclude that the analysis of social media datasets has demonstrated a limited contribution to the signal detection and signal management process. The data available in social media can complement blind spots in traditional pharmacovigilance datasets and provide significant value for targeted investigations and studies such as those relating to abuse, misuse, use in pregnancy, and patient sentiments.
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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.