  The interest in demographic information retrieval based on text data has
increased in the research community because applications have shown success in
different sectors such as security, marketing, heath-care, and others.
Recognition and identification of demographic traits such as gender, age,
location, or personality based on text data can help to improve different
marketing strategies. For instance it makes it possible to segment and to
personalize offers, thus products and services are exposed to the group of
greatest interest. This type of technology has been discussed widely in
documents from social media. However, the methods have been poorly studied in
data with a more formal structure, where there is no access to emoticons,
mentions, and other linguistic phenomena that are only present in social media.
This paper proposes the use of recurrent and convolutional neural networks, and
a transfer learning strategy for gender recognition in documents that are
written in informal and formal languages. Models are tested in two different
databases consisting of Tweets and call-center conversations. Accuracies of up
to 75\% are achieved for both databases. The results also indicate that it is
possible to transfer the knowledge from a system trained on a specific type of
expressions or idioms such as those typically used in social media into a more
formal type of text data, where the amount of data is more scarce and its
structure is completely different.
