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Overview of the Second Social Media Mining for Health (SMM4H) Shared Tasks at AMIA 2017

The volume of data encapsulated within social media continues to grow, and, consequently, there is a growing interest in developing effective systems that can convert this data into usable knowledge. Over recent years, initiatives have been taken to enable and promote the utilization of knowledge derived from social media to perform health related tasks. These initiatives include the development of data mining systems and the preparation of datasets that can be used to train such systems. The overarching focus of the SMM4H shared tasks is to release annotated social media based health related datasets to the research community, and to compare the performances of distinct natural language processing and machine learning systems on tasks involving these datasets. The second execution of the SMM4H shared tasks comprised of three subtasks involving annotated user posts from Twitter (tweets): (i) automatic classification of tweets mentioning an adverse drug reaction (ADR) (ii) automatic classification of tweets containing reports of first-person medication intake, and (iii) automatic normalization of ADR mentions to MedDRA concepts. A total of 15 teams participated and 55 system runs were submitted. The best performing systems for tasks 2 and 3 outperformed the current state of the art systems. Introduction The second execution of the SMM4H shared tasks built on the success of the first execution of the shared task workshop, which was held at the Pacific Symposium on Biocomputing (PSB), 2016. In line with the previous shared task, the data comprised of medication mentioning posts from Twitter, which were retrieved using the Twitter public streaming API. We designed and provided annotated data for three tasks. The annotated data were made publicly available for download. The performances of participating systems were compared on blind evaluation sets for each task.