ArticleDrug safety2017
Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts.
Article in Drug safety, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 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.
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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.
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Who cites it
55 citing papers in PubMed.
- Review
- Pharmacovigilance in the digital age: gaining insight from social media data.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- Prediction of adverse drug reactions using demographic and non-clinical drug characteristics in FAERS data.Scientific reports · 2024Article
- The Value of Social Media Analysis for Adverse Events Detection and Pharmacovigilance: Scoping Review.JMIR public health and surveillance · 2024Article
- Identifying Medicine Shortages With the Twitter Social Network: Retrospective Observational Study.Journal of medical Internet research · 2024Observational
- Evaluation of the knowledge of and attitudes towards pharmacovigilance among healthcare students in China: a cross-sectional study.BMC medical education · 2024Article
- Using Large Language Models to Support Content Analysis: A Case Study of ChatGPT for Adverse Event Detection.Journal of medical Internet research · 2024Article
- The State of Use and Utility of Negative Controls in Pharmacoepidemiologic Studies.American journal of epidemiology · 2024Review
- Comparative analysis of semaglutide induced adverse reactions: Insights from FAERS database and social media reviews with a focus on oral vs subcutaneous administration.Frontiers in pharmacology · 2024Article
- Estimating Patient Satisfaction Through a Language Processing Model: Model Development and Evaluation.JMIR formative research · 2023Article
- A Pragmatic Approach to Managing Long-Term Adverse Effects in Chronic Myeloid Leukemia Treatment.Current hematologic malignancy reports · 2023Review
- Comparative Safety Profiles of Oncology Biosimilars vs. Originators in Europe: An Analysis of the EudraVigilance Database.Cancers · 2023Article
- Characteristics of Phase IV Clinical Trials in Oncology: An Analysis Using the ClinicalTrials.gov Registry Data.Current oncology (Toronto, Ont.) · 2023Article
- Pharmacovigilance and Adverse Drug Reaction Reporting among the General Public in Lithuania: A Cross-Sectional Study.Healthcare (Basel, Switzerland) · 2023Article
- Using GPT-3 to Build a Lexicon of Drugs of Abuse Synonyms for Social Media Pharmacovigilance.Biomolecules · 2023Article
- The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives.Drug design, development and therapy · 2023Article
- An industry perspective on the use of machine learning in drug and vaccine safety.Frontiers in drug safety and regulation · 2023Article
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
- Implementation of Interventions and Policies on Opioids and Awareness of Opioid-Related Harms in Canada: A Multistage Mixed Methods Descriptive Study.International journal of environmental research and public health · 2022Review
- Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning.Healthcare (Basel, Switzerland) · 2022Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe rapid expansion of the Internet and computing power in recent years has opened up the possibility of using social media for pharmacovigilance. While this general concept has been proposed by many, central questions remain as to whether social media can provide earlier warnings for rare and serious events than traditional signal detection from spontaneous report data.
objectiveOur objective was to examine whether specific product-adverse event pairs were reported via social media before being reported to the US FDA Adverse Event Reporting System (FAERS).
methodsA retrospective analysis of public Facebook and Twitter data was conducted for 10 recent FDA postmarketing safety signals at the drug-event pair level with six negative controls. Social media data corresponding to two years prior to signal detection of each product-event pair were compiled. Automated classifiers were used to identify each 'post with resemblance to an adverse event' (Proto-AE), among English language posts. A custom dictionary was used to translate Internet vernacular into Medical Dictionary for Regulatory Activities (MedDRA
findingsA total of 935,246 posts were harvested from Facebook and Twitter, from March 2009 through October 2014. The automated classifier identified 98,252 Proto-AEs. Of these, 13 posts were selected for causality assessment of product-event pairs. Clinical assessment revealed that posts had sufficient information to warrant further investigation for two possible product-event associations: dronedarone-vasculitis and Banana Boat Sunscreen--skin burns. No product-event associations were found among the negative controls. In one of the positive cases, the first report occurred in social media prior to signal detection from FAERS, whereas the other case occurred first in FAERS.
conclusionsAn efficient semi-automated approach to social media monitoring may provide earlier insights into certain adverse events. More work is needed to elaborate additional uses for social media data in pharmacovigilance and to determine how they can be applied by regulatory agencies.
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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.