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. Since there is not definitive evidence that e-cigarettes cause long-term harm, these campaigns received pro-vapors' fight back, and collected a high volume of opponent messages in social media. Thus when we analyze the feedback of anti-vaping campaigns, it is crucial to partition audience into different clusters according to their attitude. Motivated by this, in this paper, we propose the “Community Detection on Anti-vaping Campaign Audience (CodeVan)” problem and design the Sorento method to solve it. Sorento computes users' intimacy scores based on their social connections, repost relations and content similarities. Extensive experiments show the effectiveness of the Sorento algorithm.