Identifying E-cigarette Content on TikTok: Using a BERTopic Modeling Approach.

ABSTRACT:

Introduction

The use of hashtags is a common way to promote e-cigarette content on social media. Analysis of hashtags may provide insight into e-cigarette promotion on social media. However, the examination of text data is complicated by the voluminous amount of social media data. This study used machine learning approaches (ie, Bidirectional Encoder Representations from Transformers [BERT] topic modeling) to identify e-cigarette content on TikTok.

Aims and methods

We used 13 unique hashtags related to e-cigarettes (eg, #vape) for data collection. The final analytic sample included 12 573 TikTok posts. To identify the best fitting number of topic clusters, we used both quantitative (ie, coherence test) and qualitative approaches (ie, researchers checked the relevance of text

SUBMITTER: Lee J 

PROVIDER: S-EPMC12477113 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature