@inproceedings{onishi-etal-2018-dealing,
title = "Dealing with Medication Non-Adherence Expressions in {T}witter",
author = "Onishi, Takeshi and
Weissenbacher, Davy and
Klein, Ari and
O{'}Connor, Karen and
Gonzalez-Hernandez, Graciela",
editor = "Gonzalez-Hernandez, Graciela and
Weissenbacher, Davy and
Sarker, Abeed and
Paul, Michael",
booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5908",
doi = "10.18653/v1/W18-5908",
pages = "32--33",
abstract = "Through a semi-automatic analysis of tweets, we show that Twitter users not only express Medication Non-Adherence (MNA) in social media but also their reasons for not complying; further research is necessary to fully extract automatically and analyze this information, in order to facilitate the use of this data in epidemiological studies.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="onishi-etal-2018-dealing">
<titleInfo>
<title>Dealing with Medication Non-Adherence Expressions in Twitter</title>
</titleInfo>
<name type="personal">
<namePart type="given">Takeshi</namePart>
<namePart type="family">Onishi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Davy</namePart>
<namePart type="family">Weissenbacher</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ari</namePart>
<namePart type="family">Klein</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Karen</namePart>
<namePart type="family">O’Connor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Graciela</namePart>
<namePart type="family">Gonzalez-Hernandez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task</title>
</titleInfo>
<name type="personal">
<namePart type="given">Graciela</namePart>
<namePart type="family">Gonzalez-Hernandez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Davy</namePart>
<namePart type="family">Weissenbacher</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Abeed</namePart>
<namePart type="family">Sarker</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Michael</namePart>
<namePart type="family">Paul</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Brussels, Belgium</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Through a semi-automatic analysis of tweets, we show that Twitter users not only express Medication Non-Adherence (MNA) in social media but also their reasons for not complying; further research is necessary to fully extract automatically and analyze this information, in order to facilitate the use of this data in epidemiological studies.</abstract>
<identifier type="citekey">onishi-etal-2018-dealing</identifier>
<identifier type="doi">10.18653/v1/W18-5908</identifier>
<location>
<url>https://aclanthology.org/W18-5908</url>
</location>
<part>
<date>2018-10</date>
<extent unit="page">
<start>32</start>
<end>33</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Dealing with Medication Non-Adherence Expressions in Twitter
%A Onishi, Takeshi
%A Weissenbacher, Davy
%A Klein, Ari
%A O’Connor, Karen
%A Gonzalez-Hernandez, Graciela
%Y Gonzalez-Hernandez, Graciela
%Y Weissenbacher, Davy
%Y Sarker, Abeed
%Y Paul, Michael
%S Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F onishi-etal-2018-dealing
%X Through a semi-automatic analysis of tweets, we show that Twitter users not only express Medication Non-Adherence (MNA) in social media but also their reasons for not complying; further research is necessary to fully extract automatically and analyze this information, in order to facilitate the use of this data in epidemiological studies.
%R 10.18653/v1/W18-5908
%U https://aclanthology.org/W18-5908
%U https://doi.org/10.18653/v1/W18-5908
%P 32-33
Markdown (Informal)
[Dealing with Medication Non-Adherence Expressions in Twitter](https://aclanthology.org/W18-5908) (Onishi et al., EMNLP 2018)
ACL
- Takeshi Onishi, Davy Weissenbacher, Ari Klein, Karen O’Connor, and Graciela Gonzalez-Hernandez. 2018. Dealing with Medication Non-Adherence Expressions in Twitter. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task, pages 32–33, Brussels, Belgium. Association for Computational Linguistics.