ArticleDrug safety2018
Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project.
Article in Drug safety, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Contrastive learning of adverse events to provide effective and interpretable vector representations for machine-assisted pharmacovigilance.Briefings in bioinformatics · 2026Article
- Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2026Review
- Social media data and its potential for pharmacovigilance: a comparative analysis of reported prevalences regarding drug-induced gingival overgrowth (DIGO).Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Charting and Sidestepping the Pitfalls of Disproportionality Analysis.Drug safety · 2026Review
- Pharmacovigilance in the digital age: gaining insight from social media data.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- Application of a Language Model Tool for COVID-19 Vaccine Adverse Event Monitoring Using Web and Social Media Content: Algorithm Development and Validation Study.JMIR infodemiology · 2024Article
- Bidirectional Long Short-Term Memory-Based Detection of Adverse Drug Reaction Posts Using Korean Social Networking Services Data: Deep Learning Approaches.JMIR medical informatics · 2024Article
- The Value of Social Media Analysis for Adverse Events Detection and Pharmacovigilance: Scoping Review.JMIR public health and surveillance · 2024Article
- Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study.Journal of medical Internet research · 2024Article
- Using Social Media as a Source of Real-World Data for Pharmaceutical Drug Development and Regulatory Decision Making.Drug safety · 2024Article
- Estimating Patient Satisfaction Through a Language Processing Model: Model Development and Evaluation.JMIR formative research · 2023Article
- Using GPT-3 to Build a Lexicon of Drugs of Abuse Synonyms for Social Media Pharmacovigilance.Biomolecules · 2023Article
- Sample Bias in Web-Based Patient-Generated Health Data of Dutch Patients With Gastrointestinal Stromal Tumor: Survey Study.JMIR formative research · 2022Article
- Reaching beyond maximum grade: progress and future directions for modernising the assessment and reporting of adverse events in haematological malignancies.The Lancet. Haematology · 2022Review
- A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation.Journal of medical Internet research · 2022Article
- Incorporating Unstructured Patient Narratives and Health Insurance Claims Data in Pharmacovigilance: Natural Language Processing Analysis of Patient-Generated Texts About Systemic Lupus Erythematosus.JMIR public health and surveillance · 2021Article
- 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
- ADE Eval: An Evaluation of Text Processing Systems for Adverse Event Extraction from Drug Labels for Pharmacovigilance.Drug safety · 2021Article
- Complementing the US Food and Drug Administration Adverse Event Reporting System With Adverse Drug Reaction Reporting From Social Media: Comparative Analysis.JMIR public health and surveillance · 2020Article
- Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med Project.Drug safety · 2020Article
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10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INTRODUCTION AND
objectiveSocial media has been proposed as a possibly useful data source for pharmacovigilance signal detection. This study primarily aimed to evaluate the performance of established statistical signal detection algorithms in Twitter/Facebook for a broad range of drugs and adverse events.
methodsPerformance was assessed using a reference set by Harpaz et al., consisting of 62 US Food and Drug Administration labelling changes, and an internal WEB-RADR reference set consisting of 200 validated safety signals. In total, 75 drugs were studied. Twitter/Facebook posts were retrieved for the period March 2012 to March 2015, and drugs/events were extracted from the posts. We retrieved 4.3 million and 2.0 million posts for the WEB-RADR and Harpaz drugs, respectively. Individual case reports were extracted from VigiBase for the same period. Disproportionality algorithms based on the Information Component or the Proportional Reporting Ratio and crude post/report counting were applied in Twitter/Facebook and VigiBase. Receiver operating characteristic curves were generated, and the relative timing of alerting was analysed.
resultsAcross all algorithms, the area under the receiver operating characteristic curve for Twitter/Facebook varied between 0.47 and 0.53 for the WEB-RADR reference set and between 0.48 and 0.53 for the Harpaz reference set. For VigiBase, the ranges were 0.64-0.69 and 0.55-0.67, respectively. In Twitter/Facebook, at best, 31 (16%) and four (6%) positive controls were detected prior to their index dates in the WEB-RADR and Harpaz references, respectively. In VigiBase, the corresponding numbers were 66 (33%) and 17 (27%).
conclusionsOur results clearly suggest that broad-ranging statistical signal detection in Twitter and Facebook, using currently available methods for adverse event recognition, performs poorly and cannot be recommended at the expense of other pharmacovigilance activities.
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