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With the increase in popularity of deep learning models for natural languageprocessing (NLP) tasks, in the field of Pharmacovigilance, more specificallyfor the identification of Adverse Drug Reactions (ADRs), there is an inherentneed for large-scale social-media datasets aimed at such tasks. With mostresearchers allocating large amounts of time to crawl Twitter or buyingexpensive pre-curated datasets, then manually annotating by humans, theseapproaches do not scale well as more and more data keeps flowing in Twitter. Inthis work we re-purpose a publicly available archived dataset of more than 9.4billion Tweets with the objective of creating a very large dataset of drugusage-related tweets. Using existing manually curated datasets from theliterature, we then validate our filtered tweets for relevance using machinelearning methods, with the end result of a publicly available dataset of1,181,993 million tweets for public use. We provide all code and detailedprocedure on how to extract this dataset and the selected tweet ids forresearchers to use.

A large-scale Twitter dataset for drug safety applications mined from publicly existing resources