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Monitoring and Identifying Emerging e-Cigarette Brands and Flavors on Twitter: Observational Study

BACKGROUND: Flavored electronic cigarettes (e-cigarettes) have become very popular in recent years. e-Cigarette users like to share their e-cigarette products and e-cigarette use (vaping) experiences on social media. e-Cigarette marketing and promotions are also prevalent online. OBJECTIVE: This stu...

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Detalles Bibliográficos
Autores principales: Tang, Qihang, Zhou, Runtao, Xie, Zidian, Li, Dongmei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9764155/
https://www.ncbi.nlm.nih.gov/pubmed/36469415
http://dx.doi.org/10.2196/42241
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author Tang, Qihang
Zhou, Runtao
Xie, Zidian
Li, Dongmei
author_facet Tang, Qihang
Zhou, Runtao
Xie, Zidian
Li, Dongmei
author_sort Tang, Qihang
collection PubMed
description BACKGROUND: Flavored electronic cigarettes (e-cigarettes) have become very popular in recent years. e-Cigarette users like to share their e-cigarette products and e-cigarette use (vaping) experiences on social media. e-Cigarette marketing and promotions are also prevalent online. OBJECTIVE: This study aims to develop a method to identify new e-cigarette brands and flavors mentioned on Twitter and to monitor e-cigarette brands and flavors mentioned on Twitter from May 2021 to December 2021. METHODS: We collected 1.9 million tweets related to e-cigarettes between May 3, 2021, and December 31, 2021, by using the Twitter streaming application programming interface. Commercial and noncommercial tweets were characterized based on promotion-related keywords. We developed a depletion method to identify new e-cigarette brands by removing the keywords that already existed in the reference data set (Twitter data related to e-cigarettes from May 3, 2021, to August 31, 2021) or our previously identified brand list from the keywords in the target data set (e-cigarette–related Twitter data from September 1, 2021, to December 31, 2021), followed by a manual Google search to identify new e-cigarette brands. To identify new e-cigarette flavors, we constructed a flavor keyword list based on our previously collected e-cigarette flavor names, which were used to identify potential tweet segments that contain at least one of the e-cigarette flavor keywords. Tweets or tweet segments with flavor keywords but not any known flavor names were marked as potential new flavor candidates, which were further verified by a web-based search. The longitudinal trends in the number of tweets mentioning e-cigarette brands and flavors were examined in both commercial and noncommercial tweets. RESULTS: Through our developed methods, we identified 34 new e-cigarette brands and 97 new e-cigarette flavors from commercial tweets as well as 56 new e-cigarette brands and 164 new e-cigarette flavors from noncommercial tweets. The longitudinal trend of the e-cigarette brands showed that JUUL was the most popular e-cigarette brand mentioned on Twitter; however, there was a decreasing trend in the mention of JUUL over time on Twitter. Menthol flavor was the most popular e-cigarette flavor mentioned in the commercial tweets, whereas mango flavor was the most popular e-cigarette flavor mentioned in the noncommercial tweets during our study period. CONCLUSIONS: Our proposed methods can successfully identify new e-cigarette brands and flavors mentioned on Twitter. Twitter data can be used for monitoring the dynamic changes in the popularity of e-cigarette brands and flavors.
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spelling pubmed-97641552022-12-21 Monitoring and Identifying Emerging e-Cigarette Brands and Flavors on Twitter: Observational Study Tang, Qihang Zhou, Runtao Xie, Zidian Li, Dongmei JMIR Form Res Original Paper BACKGROUND: Flavored electronic cigarettes (e-cigarettes) have become very popular in recent years. e-Cigarette users like to share their e-cigarette products and e-cigarette use (vaping) experiences on social media. e-Cigarette marketing and promotions are also prevalent online. OBJECTIVE: This study aims to develop a method to identify new e-cigarette brands and flavors mentioned on Twitter and to monitor e-cigarette brands and flavors mentioned on Twitter from May 2021 to December 2021. METHODS: We collected 1.9 million tweets related to e-cigarettes between May 3, 2021, and December 31, 2021, by using the Twitter streaming application programming interface. Commercial and noncommercial tweets were characterized based on promotion-related keywords. We developed a depletion method to identify new e-cigarette brands by removing the keywords that already existed in the reference data set (Twitter data related to e-cigarettes from May 3, 2021, to August 31, 2021) or our previously identified brand list from the keywords in the target data set (e-cigarette–related Twitter data from September 1, 2021, to December 31, 2021), followed by a manual Google search to identify new e-cigarette brands. To identify new e-cigarette flavors, we constructed a flavor keyword list based on our previously collected e-cigarette flavor names, which were used to identify potential tweet segments that contain at least one of the e-cigarette flavor keywords. Tweets or tweet segments with flavor keywords but not any known flavor names were marked as potential new flavor candidates, which were further verified by a web-based search. The longitudinal trends in the number of tweets mentioning e-cigarette brands and flavors were examined in both commercial and noncommercial tweets. RESULTS: Through our developed methods, we identified 34 new e-cigarette brands and 97 new e-cigarette flavors from commercial tweets as well as 56 new e-cigarette brands and 164 new e-cigarette flavors from noncommercial tweets. The longitudinal trend of the e-cigarette brands showed that JUUL was the most popular e-cigarette brand mentioned on Twitter; however, there was a decreasing trend in the mention of JUUL over time on Twitter. Menthol flavor was the most popular e-cigarette flavor mentioned in the commercial tweets, whereas mango flavor was the most popular e-cigarette flavor mentioned in the noncommercial tweets during our study period. CONCLUSIONS: Our proposed methods can successfully identify new e-cigarette brands and flavors mentioned on Twitter. Twitter data can be used for monitoring the dynamic changes in the popularity of e-cigarette brands and flavors. JMIR Publications 2022-12-05 /pmc/articles/PMC9764155/ /pubmed/36469415 http://dx.doi.org/10.2196/42241 Text en ©Qihang Tang, Runtao Zhou, Zidian Xie, Dongmei Li. Originally published in JMIR Formative Research (https://formative.jmir.org), 05.12.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Tang, Qihang
Zhou, Runtao
Xie, Zidian
Li, Dongmei
Monitoring and Identifying Emerging e-Cigarette Brands and Flavors on Twitter: Observational Study
title Monitoring and Identifying Emerging e-Cigarette Brands and Flavors on Twitter: Observational Study
title_full Monitoring and Identifying Emerging e-Cigarette Brands and Flavors on Twitter: Observational Study
title_fullStr Monitoring and Identifying Emerging e-Cigarette Brands and Flavors on Twitter: Observational Study
title_full_unstemmed Monitoring and Identifying Emerging e-Cigarette Brands and Flavors on Twitter: Observational Study
title_short Monitoring and Identifying Emerging e-Cigarette Brands and Flavors on Twitter: Observational Study
title_sort monitoring and identifying emerging e-cigarette brands and flavors on twitter: observational study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9764155/
https://www.ncbi.nlm.nih.gov/pubmed/36469415
http://dx.doi.org/10.2196/42241
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