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INTRODUCTION AND OBJECTIVE: Social 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. METHODS: A 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. RESULTS: The 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® Preferred Terms. The 1171 product-event combinations (83.9%) were confined to four MedDRA® System Organ Classes. The 195 Tweets (18.5%) contained indication information, comprising 25 different Preferred Terms. CONCLUSIONS: A 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.