@InProceedings{klein-EtAl:2017:BioNLP17,
  author    = {Klein, Ari  and  Sarker, Abeed  and  Rouhizadeh, Masoud  and  O'Connor, Karen  and  Gonzalez, Graciela},
  title     = {Detecting Personal Medication Intake in Twitter: An Annotated Corpus and Baseline Classification System},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {136--142},
  abstract  = {Social media sites (e.g., Twitter) have been used for surveillance of drug
	safety at the population level, but studies that focus on the effects of
	medications on specific sets of individuals have had to rely on other sources
	of data. Mining social media data for this in-formation would require the
	ability to distinguish indications of personal medication in-take in this
	media. Towards that end, this paper presents an annotated corpus that can be
	used to train machine learning systems to determine whether a tweet that
	mentions a medication indicates that the individual posting has taken that
	medication at a specific time. To demonstrate the utility of the corpus as a
	training set, we present baseline results of supervised classification.},
  url       = {http://www.aclweb.org/anthology/W17-2316}
}