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

ABSTRACT:

Background

Social media platforms are critical channels for promoting e-cigarettes, particularly among youth, making analysis of their vast and diverse content essential for public health interventions. Prevalence rates of e-cigarette use are high and evidence suggests that social media are popular forums that promote e-cigarette use through direct and indirect marketing techniques. The volume and diverse nature of e-cigarette-related information on social media is challenging and may obfuscate public health prevention messaging. Traditional hand-coding methods are labor-intensive and limit scalability. In contrast, unsupervised machine learning approaches, such as topic modeling, allow for efficient analysis of large datasets, uncovering patterns and trends that manual methods can

SUBMITTER: Murthy D 

PROVIDER: S-EPMC12705039 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature