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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/E17-2022.pdf