AIS Electronic Library (AISeL) AIS Electronic Library (AISeL) Health Risks of e-cigarettes: Analysis of Twitter Data Using Topic Health Risks of e-cigarettes: Analysis of Twitter Data Using Topic Mining Mining

The recent rise of e-cigarettes and vaping products has increased concerns that another young generation may become addicted to nicotine. Recently, it becomes evident that several health issues are related to the use of e-cigarettes and vaping products. The objective of this paper is to understand and identify such health issues by collecting and analyzing social media data. The analysis reflects the most important themes and topics discussed by online user’s about e-cigarettes, vaping, and associated health issues. Using topic modeling techniques, we were able to identify several health issues related to the use of e-cigarettes and vaping products. These issues include lung diseases, coughing and breathing issues, heart related issues, throat burn, respiratory related risks, dizziness, addiction, bronchitis, and cancer. sample of the checked and validated by a human coder. The used chi-square test, t-test, and multiple logistic regression for testing the assumptions. Results showed that flavor-related posts were retweeted at a significantly higher rate by e-cigarette brands and other Twitter users than non-flavor posts. the influence of flavors on e-cigarette related information propagation on social media platform. E-cigarette–related data were collected from Facebook based on a set of keywords generated by a domain expert. A total of eleven binary variables for flavor categories are used to characterize the influence of flavors on information propagation. Topic modeling was used to extract three hidden topics namely, “details about products (product), methods of consuming e-cigarettes (method), and other related discussions (other)”. Regression models and hurdle negative binomial model were used to characterize the influence of different flavors on e-cigarette–related information propagation. Results showed that five flavors, sweet, dessert & bakery, fruits, herbs & spices, and tobacco have negative influences on e-cigarette related information propagation, and that the mention of a flavor in a post does not enhance popularity of e-cigarette related information. Furthermore, some flavors reduced the popularity of information, while a set of non-flavor related factors were associated with information propagation. Also related to flavors, Li et al. systematically mined users’ e-liquid usage patterns from online reviews using opinion summarization techniques. Online users’ reviews, consisting of the review text, an overall rating and a set of e-liquid aspect ratings were collected from JuiceDB. For data analysis, reviews were labeled positive, neutral, and negative. Data were analyzed using an ID3-based influential aspect analysis model and sentiment analysis. Results showed that flavor accuracy and value were the two most important aspects that users’ sentiments toward e-liquids, fruity and sweet were the two most popular flavors, great and sweet tastes, reasonable value, and strong throat hit users satisfied with fruity and sweet flavors.