  With the increase in popularity of deep learning models for natural language
processing (NLP) tasks, in the field of Pharmacovigilance, more specifically
for the identification of Adverse Drug Reactions (ADRs), there is an inherent
need for large-scale social-media datasets aimed at such tasks. With most
researchers allocating large amounts of time to crawl Twitter or buying
expensive pre-curated datasets, then manually annotating by humans, these
approaches do not scale well as more and more data keeps flowing in Twitter. In
this work we re-purpose a publicly available archived dataset of more than 9.4
billion Tweets with the objective of creating a very large dataset of drug
usage-related tweets. Using existing manually curated datasets from the
literature, we then validate our filtered tweets for relevance using machine
learning methods, with the end result of a publicly available dataset of
1,181,993 million tweets for public use. We provide all code and detailed
procedure on how to extract this dataset and the selected tweet ids for
researchers to use.
