E-cigarettes (vape) are now the most commonly used tobacco product among youth in the United States. Ads are claiming e-cigarettes help smokers quit, but most of them contain nicotine, which can cause addiction and harm the developing adolescent brain. Therefore, national, state, and local health organizations have proposed anti-vaping campaigns to warn the potential risks of e-cigarettes. However, there is some evidence that these products may reduce harm for adult users who reduce or quit combustible cigarette smoking, and with little evidence that e-cigarettes cause long-term harm, pro-vaping advocates have used this equivocal evidence base to oppose the anti-vaping media campaign messaging, generating a very high volume of oppositional messages on social media. Thus, when we analyze the feedback of anti-vaping campaigns, it is crucial to partition the audience into different clusters according to their attitudes and affiliations. Motivated by this, in this paper, we propose the “community detection on anti-vaping campaign audience” problem and design the “community detection based on social, repost and content relation, (Sorento)” algorithm to solve it. Sorento computes users’ intimacy scores based on their social connections, repost relations, and content similarities. The community detection results achieved by Sorento demonstrate that though anti-vaping campaigns are proposed in different areas at different times, their opponent messages are mainly posted by the same community of pro-vapors.
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