Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Tang Q

PROVIDER: S-EPMC9764155 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Tang Qihang Q Zhou Runtao R Xie Zidian Z Li Dongmei D

JMIR formative research 20221205 12

<h4>Background</h4>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.<h4>Objective</h4>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 Decembe ...[more]

PMID: 36469415

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Project description:BackgroundThe COVID-19 pandemic has affected people's daily lives and has caused economic loss worldwide. Anecdotal evidence suggests that the pandemic has increased depression levels among the population. However, systematic studies of depression detection and monitoring during the pandemic are lacking.ObjectiveThis study aims to develop a method to create a large-scale depression user data set in an automatic fashion so that the method is scalable and can be adapted to future events; verify the effectiveness of transformer-based deep learning language models in identifying depression users from their everyday language; examine psychological text features' importance when used in depression classification; and, finally, use the model for monitoring the fluctuation of depression levels of different groups as the disease propagates.MethodsTo study this subject, we designed an effective regular expression-based search method and created the largest English Twitter depression data set containing 2575 distinct identified users with depression and their past tweets. To examine the effect of depression on people's Twitter language, we trained three transformer-based depression classification models on the data set, evaluated their performance with progressively increased training sizes, and compared the model's tweet chunk-level and user-level performances. Furthermore, inspired by psychological studies, we created a fusion classifier that combines deep learning model scores with psychological text features and users' demographic information, and investigated these features' relations to depression signals. Finally, we demonstrated our model's capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic.ResultsOur fusion model demonstrated an accuracy of 78.9% on a test set containing 446 people, half of which were identified as having depression. Conscientiousness, neuroticism, appearance of first person pronouns, talking about biological processes such as eat and sleep, talking about power, and exhibiting sadness were shown to be important features in depression classification. Further, when used for monitoring the depression trend, our model showed that depressive users, in general, responded to the pandemic later than the control group based on their tweets (n=500). It was also shown that three US states-New York, California, and Florida-shared a similar depression trend as the whole US population (n=9050). When compared to New York and California, people in Florida demonstrated a substantially lower level of depression.ConclusionsThis study proposes an efficient method that can be used to analyze the depression level of different groups of people on Twitter. We hope this study can raise awareness among researchers and the public of COVID-19's impact on people's mental health. The noninvasive monitoring system can also be readily adapted to other big events besides COVID-19 and can be useful during future outbreaks.

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Dataset Information

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

Background

Objective

Methods

Results

Conclusions

Publications

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

Similar Datasets

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