Beyond Purchase Intentions: Mining Behavioral Intentions of Social-Network Users

Abstract

Advertisers, recommendation-system designers, and public-health campaigners are investing heavily in online targeting, focusing intently on social-network platforms because of their ability to identify unique subpopulations according to the users’ traits. A particularly informative trait is current behavioral intentions, which provide solid information about users’ future behaviors; yet, the ability to infer such intentions constitutes a significant risk to users’ privacy. An important task is therefore understanding to what extent we can infer behavioral intentions of social-network users solely using publicly-available data. In this article, we formulate intention inference as a time-series classification task and design novel Bayesian-network models that can capture the dynamically evolving nature of the human decision-making process by combining data and priors from multiple domains. We then extend our models to the more general case of attribute inference in the presence of scarce labeled data by introducing a new semi-supervised approach to user-modeling in social networks. We evaluate the performance of our models when used for the inference of five behavioral intentions using temporal, real-world social-network data.