  In this paper, we explore salient questions about user interests,
conversations and friendships in the Facebook social network, using a novel
latent space model that integrates several data types. A key challenge of
studying Facebook's data is the wide range of data modalities such as text,
network links, and categorical labels. Our latent space model seamlessly
combines all three data modalities over millions of users, allowing us to study
the interplay between user friendships, interests, and higher-order
network-wide social trends on Facebook. The recovered insights not only answer
our initial questions, but also reveal surprising facts about user interests in
the context of Facebook's ecosystem. We also confirm that our results are
significant with respect to evidential information from the study subjects.
