Commentary on Cohn and Colleagues: Discussions of Alcohol Use in an Online Social Network for Smoking Cessation: Analysis of Topics, Sentiment, and Social Network Centrality (ACER, 2019)

Abstract

I T IS WELL understood that cigarette smokers consume alcohol more frequently and in higher amounts than nonsmokers (Falk et al., 2006;Kahler et al., 2008Kahler et al., , 2010. In addition, among heavy drinkers in smoking cessation treatment, alcohol consumption is associated with increased risks of smoking relapse (Kahler et al., 2010). A recent study by Cohn and colleagues (2019) examined the impact of alcohol consumption during a quit attempt among individuals seeking smoking cessation support online. The investigators sought to determine the characteristics of users that posted prevalent alcohol-related topics, as well as general clarity about the type of conversations all users of the platform had about alcohol. As in previous findings, alcohol use was associated with a recurrence of tobacco use during a quit attempt. Data from social networking sites and message boards can also be used to gain psychological insights. Indeed, Cohn and colleagues (2019) observed a relationship between alcohol use and celebratory discussions of quit milestones; gained a deeper understanding of the positive sentiment toward alcohol-related content in these networks; and observed how alcohol conversations varied by users' social connectivity in the network. This is the first study to examine how co-occurring alcohol use impacts the support received (and provided) on online smoking cessation platforms. The findings by Cohn and colleagues (2019) provide the first look into user-level sentiment and discourse patterns involving alcohol in a tobacco cessation digital intervention. As digital interventions have historically mirrored the static content from physical interventions (i.e., curriculum-based static content), the results suggest that digital interventions should instead use dynamic content that is variable based upon user characteristics and real-time sentiment or linguistic analysis. For example, new users who are early on in their tobacco quit attempt were more likely to express negative sentiment toward alcohol content, as well as express cravings and temptations parallel with both alcohol and tobacco. Compared to more experienced users, who were abstinent from tobacco for a longer period of time and more socially connected on the platform, this shows a categorical difference in both needed intervention content-such as material focused on cravings and temptations for new users-and helping users grow their social connections, while minimizing potentially emotionally triggering content from more experienced users who were often virtually toasting.The use of technology to better understand and intervene in substance use behaviors, including both licit substances (e.g., tobacco and alcohol) and illicit substances (e.g., nonprescription opioids and cannabis), has become increasingly common (Dedert et al., 2015;Nesv ag and McKay, 2018). In part, this movement to integrate technology into addiction treatment is a result of high treatment demand, limited treatment availability, and a growing familiarity of the treatment populatio...