  Author Profiling (AP) aims at predicting specific characteristics from a
group of authors by analyzing their written documents. Many research has been
focused on determining suitable features for modeling writing patterns from
authors. Reported results indicate that content-based features continue to be
the most relevant and discriminant features for solving this task. Thus, in
this paper, we present a thorough analysis regarding the appropriateness of
different distributional term representations (DTR) for the AP task. In this
regard, we introduce a novel framework for supervised AP using these
representations and, supported on it. We approach a comparative analysis of
representations such as DOR, TCOR, SSR, and word2vec in the AP problem. We also
compare the performance of the DTRs against classic approaches including
popular topic-based methods. The obtained results indicate that DTRs are
suitable for solving the AP task in social media domains as they achieve
competitive results while providing meaningful interpretability.
