![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
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.
Acknowledgments
We wish to thank Professor Joan Feigenbaum for her guidance and Professor Sekhar Tatikonda for his initial assistance.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
Additional information
Funding
Notes on contributors
Lihi Idan
Lihi Idan is a computer-science researcher with a PhD from Yale University. Her current research interests are at the intersection of Machine Learning, Privacy and Human-Computer Interaction; Bayesian Causal Inference; and Applied Cryptography.