Categorizing E-cigarette-related tweets using BERT topic modeling

Results underscore the possibility of leveraging BERTopic modeling to reduce large quantities of data to comprehensively describe and categorize myriad e-cigarette related messages to which social media users are exposed. This data reduction approach can be applied to various social media platforms to describe and categorize e-cigarette posts and thereby triangulate and validate findings. Thematic content analysis of the topics identified through this technique requires supervision and human inputs. Our approach provides a comprehensive understanding of the evolving e-cigarette discourse, informing public health counter-messaging and policy interventions. Moreover, findings support the need for regulation, such as reducing appealing flavors and suggest that social media can be used effectively to support public health messaging (i.e., quitting messages).

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Publication Details

Journal
Emerging Trends in Drugs Addictions and Health
Published
2024-10-05
DOI
https://doi.org/10.1016/j.etdah.2024.100160
Citations
4
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
7.58

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article

Categorizing E-cigarette-related tweets using BERT topic modeling

Steven J. Schwartz, Emily T. Hébert, Anna V. Wilkinson, Alexandra Loukas et al.
4 citations
Emerging Trends in Drugs Addictions and Health
Computational and Text Analysis Methods
7.58
article

Categorizing E-cigarette-related tweets using BERT topic modeling

Steven J. Schwartz, Emily T. Hébert, Anna V. Wilkinson, Alexandra Loukas, Qingping Yang, Melissa B. Harrell, Shuvam Keshari, Sonia Arora, Dr B V Ramana Murthy -
article en
4 citations

Abstract

Results underscore the possibility of leveraging BERTopic modeling to reduce large quantities of data to comprehensively describe and categorize myriad e-cigarette related messages to which social media users are exposed. This data reduction approach can be applied to various social media platforms to describe and categorize e-cigarette posts and thereby triangulate and validate findings. Thematic content analysis of the topics identified through this technique requires supervision and human inputs. Our approach provides a comprehensive understanding of the evolving e-cigarette discourse, informing public health counter-messaging and policy interventions. Moreover, findings support the need for regulation, such as reducing appealing flavors and suggest that social media can be used effectively to support public health messaging (i.e., quitting messages).

Emerging Trends in Drugs Addictions and HealthVol. 4
Austin College (US), Texas Christian University (US), The University of Texas Health Science Center (US), The University of Texas at Austin (US), The University of Texas Health Science Center at Houston (US)
Foundation for the National Institutes of Health
Openalex Percentile: Top 2%
Computational and Text Analysis Methods
7.58
DOIOpen Access PDFOpenAlex
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Categorizing E-cigarette-related tweets using BERT topic modeling — Steven J. Schwartz, Emily T. Hébert, et al. · Emerging Trends in Drugs Addictions and Health (2024) | TGRS Research Map | TGRS