  The goal of Author Profiling (AP) is to identify demographic aspects (e.g.,
age, gender) from a given set of authors by analyzing their written texts.
Recently, the AP task has gained interest in many problems related to computer
forensics, psychology, marketing, but specially in those related with social
media exploitation. As known, social media data is shared through a wide range
of modalities (e.g., text, images and audio), representing valuable information
to be exploited for extracting valuable insights from users. Nevertheless, most
of the current work in AP using social media data has been devoted to analyze
textual information only, and there are very few works that have started
exploring the gender identification using visual information. Contrastingly,
this paper focuses in exploiting the visual modality to perform both age and
gender identification in social media, specifically in Twitter. Our goal is to
evaluate the pertinence of using visual information in solving the AP task.
Accordingly, we have extended the Twitter corpus from PAN 2014, incorporating
posted images from all the users, making a distinction between tweeted and
retweeted images. Performed experiments provide interesting evidence on the
usefulness of visual information in comparison with traditional textual
representations for the AP task.
