Document Type
Article
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 popular machine learning and deep learning algorithms, including Naïve Bayes, Support Vector Machine, Random Forest, XGBoost, Multilayer Perception, Transformer Neural Network, and stacking and voting ensemble models to build our customized classification model. We extracted a set of sample tweets during an outbreak of e-cigarette or vaping-related lung injury (EVALI) in 2019 and created an annotated corpus to train and evaluate these models. After comparing the performance of each model, we found that the stacking ensemble learning achieved the highest performance with an F1-score of 0.97. All models could achieve 0.90 or higher after tuning hyperparameters. The ensemble learning model has the best average performance. Our study findings provide informative guidelines and practical implications for the automated detection of themed social media data for public opinions and health surveillance purposes.
Digital Object Identifier (DOI)
Publication Info
Published in Frontiers in Big Data, Volume 5, 2022, pages 770585-.
APA Citation
Ren, Y., Wu, D., Singh, A., Kasson, E., Huang, M., & Cavazos-Rehg, P. (2022). Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning Classification. Frontiers in Big Data, 5.https://doi.org/10.3389/fdata.2022.770585
Rights
© 2022 Ren, Wu, Singh, Kasson, Huang and Cavazos-Rehg. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) .The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.