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Updated: Jan 8, 2026

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Categorizing E-cigarette-related tweets using BERT topic modeling.

D Murthy1, S Keshari2, S Arora3

  • 1Professor of Media Studies, Sociology, and Information, University of Texas at Austin, United States of America.

Emerging Trends in Drugs, Addictions, and Health
|December 17, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning effectively categorized over 100,000 e-cigarette tweets into distinct themes, aiding public health interventions. This analysis of social media discourse on vaping provides insights for counter-messaging and policy development.

Keywords:
BertopicE-cigarettesNatural language processingTopic modelingVape

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Area of Science:

  • Public Health
  • Computational Social Science
  • Digital Health

Background:

  • Social media platforms are key channels for e-cigarette promotion, especially among youth.
  • Analyzing diverse social media content is crucial for public health interventions regarding e-cigarette use.
  • Traditional content analysis methods are labor-intensive and lack scalability for large datasets.

Purpose of the Study:

  • To assess the effectiveness of machine learning, specifically topic modeling, in categorizing e-cigarette-related tweets.
  • To identify key themes and patterns in social media discussions about e-cigarettes.
  • To inform public health counter-messaging and policy interventions through a better understanding of online discourse.

Main Methods:

  • Utilized BERTopic modeling to derive and cluster vape-related tweets.
  • Conducted qualitative content analysis on clustered tweets for thematic understanding.
  • Employed automated geoparsing to infer geographic locations of e-cigarette conversations.

Main Results:

  • Successfully identified over 100,000 tweets categorized into distinct themes in English and Spanish.
  • Six primary topics were identified: Flavors/Disposable Vapes, Cannabis, Vape Shops/Refillable Vapes, Vape Culture, Anti-vaping/Quitting, and Spanish Tweets/Nicotine Vaping.
  • Geoparsing indicated the United States as the location with the highest volume of vaping-related tweets.

Conclusions:

  • Machine learning, particularly BERTopic, can efficiently reduce and categorize large volumes of social media data on e-cigarettes.
  • The identified themes provide a comprehensive understanding of the evolving e-cigarette discourse.
  • Findings support the need for regulations (e.g., flavor restrictions) and highlight social media's potential for public health messaging, such as smoking cessation campaigns.