Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning Classification.

ABSTRACT: There are increasingly strict regulations surrounding the purchase and use of combustible tobacco products (i.e., cigarettes); simultaneously, the use of other tobacco products, including e-cigarettes (i.e., vaping products), has dramatically increased. However, public attitudes toward vaping vary widely, and the health effects of vaping are still largely unknown. As a popular social media, Twitter contains rich information shared by users about their behaviors and experiences, including opinions on vaping. It is very challenging to identify vaping-related tweets to source useful information manually. In the current study, we proposed to develop a detection model to accurately identify vaping-related tweets using machine learning and deep learning methods. Specifically, we applied seven po

SUBMITTER: Ren Y 

PROVIDER: S-EPMC8866955 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature