Identifying Topics Around Nicotine Gum: A Machine Learning Approach with Twitter Data.

Abstract

Background: Nicotine gum products from brands like Lucy and Rogue are relatively new arrivals to the tobacco marketplace. While studies of correlates of nicotine gum use are in their nascent stage, data from social media can be used to stay abreast of user experiences with novel tobacco products. This study leveraged machine learning to identify topics of Twitter posts about nicotine gum from the year 2022.

Methods: Twitter data was collected using the Twitter Application Programming Interface (API), with search terms "nicotine" AND "gum" OR "nic" AND "gum". 16,940 tweets from 10,353 unique users were included in the analysis. Topic modeling with Top2Vec was used to identify topics and a string search of popular brands and flavors was also conducted.

Results: Eight distinct topics were identified. Smoking and vaping cessation was the most common topic, followed by promotion, pricing, and marketing; appeal; product comparisons; perceived benefits; distrust in institutions; health concerns; and COVID-19 misinformation. The most mentioned brand was Nicorette. A tenth of the tweets contained a reference to a flavor.

Conclusion: The goal of identifying topics in nicotine gum-related conversations is to better understand the public's perceptions and experiences with nicotine gum. These findings may be used to inform survey-based research, policy targets, and health communication campaigns.