Dealing with Medication Non-Adherence Expressions in Twitter

Takeshi Onishi, Davy Weissenbacher, Ari Klein, Karen O’Connor, Graciela Gonzalez-Hernandez


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.
Anthology ID:
W18-5908
Volume:
Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task
Month:
October
Year:
2018
Address:
Brussels, Belgium
Editors:
Graciela Gonzalez-Hernandez, Davy Weissenbacher, Abeed Sarker, Michael Paul
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
32–33
Language:
URL:
https://aclanthology.org/W18-5908
DOI:
10.18653/v1/W18-5908
Bibkey:
Cite (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.
Cite (Informal):
Dealing with Medication Non-Adherence Expressions in Twitter (Onishi et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-5908.pdf