A Computational Analysis of the Language of Drug Addiction

Carlo Strapparava, Rada Mihalcea


Abstract
We present a computational analysis of the language of drug users when talking about their drug experiences. We introduce a new dataset of over 4,000 descriptions of experiences reported by users of four main drug types, and show that we can predict with an F1-score of up to 88% the drug behind a certain experience. We also perform an analysis of the dominant psycholinguistic processes and dominant emotions associated with each drug type, which sheds light on the characteristics of drug users.
Anthology ID:
E17-2022
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Mirella Lapata, Phil Blunsom, Alexander Koller
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
136–142
Language:
URL:
https://aclanthology.org/E17-2022
DOI:
Bibkey:
Cite (ACL):
Carlo Strapparava and Rada Mihalcea. 2017. A Computational Analysis of the Language of Drug Addiction. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 136–142, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
A Computational Analysis of the Language of Drug Addiction (Strapparava & Mihalcea, EACL 2017)
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PDF:
https://aclanthology.org/E17-2022.pdf