Cargando…
Characterizing alternative and emerging tobacco product transition of use behavior on Twitter
OBJECTIVE: The objective of this study was to develop an inductive coding approach specific to characterizing user-generated social media conversations about transition of use of different tobacco and alternative and emerging tobacco products (ATPs). RESULTS: A total of 40,206 tweets were collected...
Autores principales: | Bardier, Cortni, Yang, Joshua S., Li, Jiawei, Mackey, Tim K. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351350/ https://www.ncbi.nlm.nih.gov/pubmed/34372926 http://dx.doi.org/10.1186/s13104-021-05719-0 |
Ejemplares similares
-
Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram
por: Mackey, Tim Ken, et al.
Publicado: (2020) -
Characterizing Self-Reported Tobacco, Vaping, and Marijuana-Related Tweets Geolocated for California College Campuses
por: Cuomo, Raphael E., et al.
Publicado: (2021) -
Campus Smoking Policies and Smoking-Related Twitter Posts Originating From California Public Universities: Retrospective Study
por: Yang, Joshua S, et al.
Publicado: (2021) -
Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study
por: Mackey, Tim, et al.
Publicado: (2020) -
Detection of illicit online sales of fentanyls via Twitter
por: Mackey, Tim K., et al.
Publicado: (2017)