Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning Classification Article Swipe
Yang Ren
,
Dezhi Wu
,
Avineet Kumar Singh
,
Erin Kasson
,
Ming Huang
,
Patricia Cavazos‐Rehg
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fdata.2022.770585
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fdata.2022.770585
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fdata.2022.770585
- OA Status
- gold
- Cited By
- 6
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4211235060
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4211235060Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fdata.2022.770585Digital Object Identifier
- Title
-
Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-02-10Full publication date if available
- Authors
-
Yang Ren, Dezhi Wu, Avineet Kumar Singh, Erin Kasson, Ming Huang, Patricia Cavazos‐RehgList of authors in order
- Landing page
-
https://doi.org/10.3389/fdata.2022.770585Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3389/fdata.2022.770585Direct OA link when available
- Concepts
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Machine learning, Artificial intelligence, Computer science, Social media, Ensemble learning, Random forest, Hyperparameter, Naive Bayes classifier, Deep learning, Support vector machine, World Wide WebTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3, 2022: 2Per-year citation counts (last 5 years)
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57Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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