  A variety of approaches have been proposed to automatically infer the
profiles of users from their digital footprint in social media. Most of the
proposed approaches focus on mining a single type of information, while
ignoring other sources of available user-generated content (UGC). In this
paper, we propose a mechanism to infer a variety of user characteristics, such
as, age, gender and personality traits, which can then be compiled into a user
profile. To this end, we model social media users by incorporating and
reasoning over multiple sources of UGC as well as social relations. Our model
is based on a statistical relational learning framework using Hinge-loss Markov
Random Fields (HL-MRFs), a class of probabilistic graphical models that can be
defined using a set of first-order logical rules. We validate our approach on
data from Facebook with more than 5k users and almost 725k relations. We show
how HL-MRFs can be used to develop a generic and extensible user profiling
framework by leveraging textual, visual, and relational content in the form of
status updates, profile pictures and Facebook page likes. Our experimental
results demonstrate that our proposed model successfully incorporates multiple
sources of information and outperforms competing methods that use only one
source of information or an ensemble method across the different sources for
modeling of users in social media.
