ArticleDrug safety2014
Digital drug safety surveillance: monitoring pharmaceutical products in twitter.
Article in Drug safety, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 123 papers, 4 of them syntheses 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
123 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.International journal of medical informatics · 2019Pooled it
- Systematic review of surveillance by social media platforms for illicit drug use.Journal of public health (Oxford, England) · 2017Pooled it
- Medication-indication knowledge bases: a systematic review and critical appraisal.Journal of the American Medical Informatics Association : JAMIA · 2015Pooled it
- Systematic review on the prevalence, frequency and comparative value of adverse events data in social media.British journal of clinical pharmacology · 2015Pooled it
- Waldo: Automated discovery of adverse events from unstructured self reports.PLOS digital health · 2025Article
- Pharmacovigilance in the Age of Legalized Cannabis: Using Social Media to Monitor Drug-Drug Interactions Between Immunosuppressants and Cannabis-Derived Products.Drug safety · 2025Article
- Pharmacovigilance in the digital age: gaining insight from social media data.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- Predicting Drugs Suspected of Causing Adverse Drug Reactions Using Graph Features and Attention Mechanisms.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Purpose-oriented review of public health surveillance systems: use of surveillance systems and recent advances.BMJ public health · 2024Review
- Using Social Media as a Source of Real-World Data for Pharmaceutical Drug Development and Regulatory Decision Making.Drug safety · 2024Article
- Digital Technology Applications in the Management of Adverse Drug Reactions: Bibliometric Analysis.Pharmaceuticals (Basel, Switzerland) · 2024Article
- The "Okusuri Charm" movement in Japan: Prescription drug accessories emerging on X (Twitter).Journal of general and family medicine · 2024Article
- Article
- 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
- Self-reported adverse events associated with ∆Journal of cannabis research · 2023Article
- Patient-centered pharmacovigilance: priority actions from the inherited bleeding disorders community.Therapeutic advances in drug safety · 2023Review
- An easy numeric data augmentation method for early-stage COVID-19 tweets exploration of participatory dynamics of public attention and news coverage.Information processing & management · 2022Article
- Artificial Intelligent Context-Aware Machine-Learning Tool to Detect Adverse Drug Events from Social Media Platforms.Journal of medical toxicology : official journal of the American College of Medical Toxicology · 2022Article
- Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning.Healthcare (Basel, Switzerland) · 2022Article
- Influenza and Measles-MMR: two case study of the trend and impact of vaccine-related Twitter posts in Spanish during 2015-2018.Human vaccines & immunotherapeutics · 2022Article
63 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTraditional adverse event (AE) reporting systems have been slow in adapting to online AE reporting from patients, relying instead on gatekeepers, such as clinicians and drug safety groups, to verify each potential event. In the meantime, increasing numbers of patients have turned to social media to share their experiences with drugs, medical devices, and vaccines.
objectiveThe aim of the study was to evaluate the level of concordance between Twitter posts mentioning AE-like reactions and spontaneous reports received by a regulatory agency.
methodsWe collected public English-language Twitter posts mentioning 23 medical products from 1 November 2012 through 31 May 2013. Data were filtered using a semi-automated process to identify posts with resemblance to AEs (Proto-AEs). A dictionary was developed to translate Internet vernacular to a standardized regulatory ontology for analysis (MedDRA(®)). Aggregated frequency of identified product-event pairs was then compared with data from the public FDA Adverse Event Reporting System (FAERS) by System Organ Class (SOC).
resultsOf the 6.9 million Twitter posts collected, 4,401 Proto-AEs were identified out of 60,000 examined. Automated, dictionary-based symptom classification had 86 % recall and 72 % precision [corrected]. Similar overall distribution profiles were observed, with Spearman rank correlation rho of 0.75 (p < 0.0001) between Proto-AEs reported in Twitter and FAERS by SOC.
conclusionPatients reporting AEs on Twitter showed a range of sophistication when describing their experience. Despite the public availability of these data, their appropriate role in pharmacovigilance has not been established. Additional work is needed to improve data acquisition and automation.
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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.