document: Audience selection for on-line brand advertising : privacy-friendly social network targeting This paper describes and evaluates privacy-friendly methods for extracting quasi-social networks from browser behavior on user-generated content sites , for the purpose of finding good audiences for brand advertising ( as opposed to click maximizing , for example ) . Targeting social-network neighbors resonates well with advertisers , and on-line browsing behavior data counterintuitively can allow the identification of good audiences anonymously . Besides being one of the first papers to our knowledge on data mining for on-line brand advertising , this paper makes several important contributions . We introduce a framework for evaluating brand audiences , in analogy to predictive-modeling holdout evaluation . We introduce methods for extracting quasi-social networks from data on visitations to social networking pages , without collecting any information on the identities of the browsers or the content of the social-network pages . We introduce measures of brand proximity in the network , and show that audiences with high brand proximity indeed show substantially higher brand affinity . Finally , we provide evidence that the quasi-social network embeds a true social network , which along with results from social theory offers one explanation for the increase in brand affinity of the selected audiences .
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