Sentiment analysis to determine the impact of online messages on smokers' choices to use varenicline

Abstract

Background: Social networks are a prominent component of online smoking cessation interventions. This study applied sentiment analysis-a data processing technique that codes textual data for emotional polarity-to examine how exposure to messages about the cessation drug varenicline affects smokers' decision making around its use. Methods: Data were from QuitNet, an online social network dedicated to smoking cessation and relapse prevention. Self-reported medication choice at registration and at 30 days was coded among new QuitNet registrants who participated in at least one forum discussion mentioning varenicline between January 31, 2005 and March 9, 2008. Commercially available software was used to code the sentiment of forum messages mentioning varenicline that occurred during this time frame. Logistic regression analyses examined whether forum message exposure predicted medication choice. Results: The sample of 2132 registrants comprised mostly women (78.3%), white participants (83.4%), averaged 41.2 years of age (SD = 10.9), and smoked on average 21.5 (SD = 9.7) cigarettes/day. After adjusting for potential confounders, as exposure to positive varenicline messages outweighed negative messages, the odds of switching to varenicline (odds ratio = 2.05, 95% confidence interval = 1.66 to 2.54) and continuing to use varenicline (odds ratio = 2.46, 95% confidence interval = 1.96 to 3.10) statistically significantly increased. Conclusions: Sentiment analysis is a useful tool for analyzing text-based data to examine their impact on behavior change. Greater exposure to positive sentiment in online conversations about varenicline is associated with a greater likelihood that smokers will choose to use varenicline in a quit attempt. © The Author 2013. Published by Oxford University Press. All rights reserved.