Tang Qihang, Zhou Runtao, Xie Zidian, Li Dongmei
Goergen Institute for Data Science, University of Rochester, Rochester, NY, United States.
Department of Clinical & Translational Research, University of Rochester Medical Center, Rochester, NY, United States.
JMIR Form Res. 2022 Dec 5;6(12):e42241. doi: 10.2196/42241.
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.
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.
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.
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.
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.
调味电子烟近年来变得非常流行。电子烟使用者喜欢在社交媒体上分享他们的电子烟产品和使用（吸食）体验。电子烟的营销和推广在网上也很普遍。
本研究旨在开发一种方法来识别推特上提到的新电子烟品牌和口味，并监测2021年5月至2021年12月期间推特上提到的电子烟品牌和口味。
我们使用推特流式应用程序编程接口，在2021年5月3日至2021年12月31日期间收集了190万条与电子烟相关的推文。基于与推广相关的关键词对商业和非商业推文进行特征描述。我们开发了一种剔除方法，通过从目标数据集中（2021年9月1日至2021年12月31日与电子烟相关的推特数据）的关键词中去除参考数据集中（2021年5月3日至2021年8月31日与电子烟相关的推特数据）已经存在的关键词或我们之前确定的品牌列表，来识别新的电子烟品牌，随后进行人工谷歌搜索以识别新的电子烟品牌。为了识别新的电子烟口味，我们根据之前收集的电子烟口味名称构建了一个口味关键词列表，该列表用于识别包含至少一个电子烟口味关键词的潜在推文片段。带有口味关键词但没有任何已知口味名称的推文或推文片段被标记为潜在的新口味候选，通过基于网络的搜索进一步验证。在商业和非商业推文中都研究了提及电子烟品牌和口味的推文数量的纵向趋势。
通过我们开发的方法，我们从商业推文中识别出34个新的电子烟品牌和97种新的电子烟口味，从非商业推文中识别出56个新的电子烟品牌和164种新的电子烟口味。电子烟品牌的纵向趋势表明，JUUL是推特上提到的最受欢迎的电子烟品牌；然而，随着时间的推移，推特上提及JUUL的趋势呈下降趋势。在我们的研究期间，薄荷醇口味是商业推文中提到的最受欢迎的电子烟口味，而芒果口味是非商业推文中提到的最受欢迎的电子烟口味。
我们提出的方法可以成功识别推特上提到的新电子烟品牌和口味。推特数据可用于监测电子烟品牌和口味受欢迎程度的动态变化。
