Detecting Personal Medication Intake in Twitter: An Annotated Corpus and Baseline Classification System

Ari Klein, Abeed Sarker, Masoud Rouhizadeh, Karen O’Connor, Graciela Gonzalez


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.
Anthology ID:
W17-2316
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
136–142
Language:
URL:
https://www.aclweb.org/anthology/W17-2316
DOI:
10.18653/v1/W17-2316
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2316.pdf