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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Stimulants are substances that enhance neural activity and elevate dopamine levels in the brain, leading to their highly addictive nature. These drugs include cocaine, amphetamines, MDMA, caffeine, and nicotine, each with distinct mechanisms of action and varied health implications.
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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
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Related Experiment Video

Updated: Jan 8, 2026

Generation of Electronic Cigarette Aerosol by a Third-Generation Machine-Vaping Device: Application to Toxicological Studies
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Generation of Electronic Cigarette Aerosol by a Third-Generation Machine-Vaping Device: Application to Toxicological Studies

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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.