UC San Diego

The aims of the study were to characterize racial and ethnic disparities amongst electronic cigarette users through detecting and classifying user generated conversations associated with electronic cigarettes use on social media platforms. The investigative approach was through a literature review, analyses of NHANES data, and data collection of geocoded tweets from Twitter. A total of 5,718 tweets were collected. Using a topic modeling approach called BTM, the study identified relevant clusters of Twitter conversation related to electronic cigarettes, 348 tweets were identified that included conversations about user behavior. These tweets were grouped into three categories and visualized on a map to see where conversations were located for racial ethnic associations. The results of the study provide insights into organic conversations regarding user behavior associated with electronic cigarettes. Future studies should focus on other themes and topics associated with electronic cigarettes on social platforms to inform health communication and global public health efforts towards addressing tobacco and nicotine addiction and prevention.