ArticleDrug safety2020
Adverse Events in Twitter-Development of a Benchmark Reference Dataset: Results from IMI WEB-RADR.
Article in Drug safety, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed.
- Detecting Adverse Drug Events in Social Media: A Brief Literature Review.SN computer science · 2026Article
- Opinion Mining of Erowid's Experience Reports on LSD and Psilocybin-Containing Mushrooms.Drug safety · 2025Article
- Pharmacovigilance in the digital age: gaining insight from social media data.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Article
- An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis.JMIR medical informatics · 2022Article
- DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Prospective Evaluation of Adverse Event Recognition Systems in Twitter: Results from the Web-RADR Project.Drug safety · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INTRODUCTION AND
objectiveSocial media has been suggested as a source for safety information, supplementing existing safety surveillance data sources. This article summarises the activities undertaken, and the associated challenges, to create a benchmark reference dataset that can be used to evaluate the performance of automated methods and systems for adverse event recognition.
methodsA retrospective analysis of public English-language Twitter posts (Tweets) was performed. We sampled 57,473 Tweets out of 5,645,336 Tweets created between 1 March, 2012 and 1 March, 2015 that mentioned at least one of six medicinal products of interest (insulin glargine, levetiracetam, methylphenidate, sorafenib, terbinafine, zolpidem). Products, adverse events, indications, product-event combinations, and product-indication combinations were extracted and coded by two independent teams of safety reviewers.
resultsThe benchmark reference dataset consisted of 1056 positive controls ("adverse event Tweets") and 56,417 negative controls ("non-adverse event Tweets"). The 1056 adverse event Tweets contained 1396 product-event combinations referring to personal adverse event experiences, comprising 292 different MedDRA
conclusionsA manually curated benchmark reference dataset based on Twitter data has been created and is made available to the research community to evaluate the performance of automated methods and systems for adverse event recognition in unstructured free-text information.
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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.