Strategies to find audience segments on Twitter for e-cigarette education campaigns

Highlights

• Audience delineation improve e-cigs public health campaigns.

• Network analysis can support audience segmentation on social media.

• Tightly clustered groups are more likely to have strong sentiment.

• Public health officials can leverage these methods for targeted health messaging.

Abstract

The development of public health education campaigns about tobacco products requires an understanding of specific audience segments including their views, intentions, use of media, perceived barriers, and benefits of change. For example, identifying and targeting individuals who express ambivalence about e-cigarette use on Twitter may be helpful in devising and focusing public health campaigns to reduce e-cigarette use. This study developed a novel analytic strategy using social network analysis to identify audience segments on Twitter based on positive, negative, and neutral e-cigarette sentiment. Using Twitter data collected from April 2015 to March 2016, we identified different sub-groups of users who retweeted about e-cigarettes, and measured each sub-group's clustering coefficient (CC), which describes how tightly people cluster together. Ten high CC and ten low CC groups were randomly selected; then 100 randomly selected tweets from each group were coded for e-cigarette sentiment (positive, negative, neutral). Results indicate that differences in e-cigarette sentiment are associated with clustering of Twitter network ties. Statistical analyses revealed that high CC groups were more likely to have strong e-cigarette sentiments, suggesting that tightly clustered groups may be “echo chambers” (i.e., like-minded people repeating the same messages). By contrast, low CC groups were more likely to have neutral sentiments, and had greater fluctuation in sentiment over time, suggesting that they may be more flexible in their opinions about e-cigarettes and may be particularly receptive to targeted public health campaigns. Informatics techniques such as determination of clusters using social network analysis can be useful in identifying audience segments for future public health campaigns.

Introduction

Recent evidence suggests that the use of e-cigarettes (a non-combustible tobacco product) is increasing (Syamlal, Jamal, King, & Mazurek, 2016) and may pose significant health risks (McConnell et al., 2016; Office of the Surgeon General, 2016), including an increased risk of future combustible cigarette use in adolescents (Barrington-Trimis et al., 2016; Leventhal et al., 2015; Primack, Soneji, Stoolmiller, Fine, & Sargent, 2015; Soneji et al., 2017). As social media use continues to increase (Greenwood, Perrin, & Duggan, 2016), several stakeholders (such as the tobacco industry, public health agencies, and users of tobacco products) have used a range of social media platforms to disseminate information about these products. For example, the tobacco industry, including combustible cigarette and e-cigarette brands, has taken advantage of marketing on Twitter, Instagram and other platforms (Allem et al., 2018; Allem, Escobedo, Chu, Cruz, & Unger, 2017; Centers for Disease Control and Prevention, 2016; Chu et al., 2015; Chu, Allem, Cruz, & Unger, 2017). Additionally, recent public health campaigns have used Twitter to spread messages that stress the dangers of nicotine addiction. On the other hand, anti-tobacco messages that are widely distributed can elicit negative reactions in viewers with strong opinions (such as current e-cigarette users and independent vendors), leading to counter messaging (Allem et al., 2016; Harris et al., 2014). Overall, there is a need for the public health community to develop strategies to effectively disseminate evidence-based information regarding e-cigarettes to the populations they serve.

Social media platforms can be used to send tailored health education messages about tobacco to groups that would benefit from these messages (Thackeray, Neiger, & Keller, 2012). This kind of targeted approach requires identification of audience segments that hold similar views and readiness to change. Social media is an arena in which users may encounter messages that can influence their behaviors and attitudes. The Pew Research Center recently found that approximately 20% of social media users changed their minds about an issue because of something they saw on social media (Duggan & Smith, 2016). Further, sending and receiving pro-smoking social media messages is positively associated with “offline” smoking intentions and attitudes in college students (Depue, Southwell, Betzner, & Walsh, 2015; Yoo, Yang, & Cho, 2016). If public health researchers could identify different audience segments on social media, then it is possible they could devise targeted public health campaigns for that specific population. Individuals who are already hold strongly anti-tobacco opinions are unlikely to need convincing, and individuals who already hold strongly pro-tobacco opinions are unlikely to be convinced by a social media campaign. Therefore, it is possible that health communication campaigns would be most effective in changing opinions (and also cost-efficient), if they focused on individuals in the middle (i.e., those that hold – or at are exposed to – neutral opinions, ambivalent opinions, or no opinions at all). This communication strategy would be consistent with recent evidence indicating that providing information about anti-tobacco social norms decreases tobacco attitudes for emerging adults exposed to ambivalent tobacco messaging (Hohman, Crano, & Niedbala, 2016). However, to our knowledge, there is a noticeable gap in the literature on methods to segment audiences in social media for public health.

In the current study, we examine a novel method of identifying segments of Twitter users based on their sentiment toward e-cigarettes (i.e., positive, negative, or neutral opinions about e-cigarettes). The majority of extant Twitter studies (including those described above) have operationalized constructs such as dissemination of messages and influence of Twitter users with metrics available from Twitter such as hashtag usage (Rattanaritnont, Toyoda, & Kitsuregawa, 2012), person tagging, i.e., being mentioned by another person in a tweet (Cha, Haddadi, Benevenuto, & Gummadi, 2010), or retweets, i.e., forwarding tweets to the user's network of followers (Kupavskii et al., 2012). The number of followers or retweets might represent popularity, and a health campaign could target popular users to spread a particular message. However, relying entirely on Twitter metrics to study human behavior could be limiting as these metrics (e.g., retweets) have no basis outside of Twitter.

Here we present a novel, alternative method applying concepts and metrics from social network analysis to further understand audience segmentation on Twitter. We will examine sub-groups of Twitter users to study how clustering may be associated with positive, negative, and neutral sentiment toward e-cigarettes. Twitter tends to contain “echo chambers,” where like-minded people repeat the same messages back and forth to each other (Barberá, Jost, Nagler, Tucker, & Bonneau, 2015). As people are more likely to choose to follow friends in networks that are similar to one another (De Choudhury, 2011), we hypothesize that tightly connected groups will have a higher proportion of positive or negative sentiment than loosely connected groups; in other words, echo chambers will have a lot of one-sided sentiment. Additionally, we hypothesize that loosely connected groups will have greater fluctuation in sentiment over time.