  In this paper we describe a dynamic normalization process applied to social
network multilingual documents (Facebook and Twitter) to improve the
performance of the Author profiling task for short texts. After the
normalization process, $n$-grams of characters and n-grams of POS tags are
obtained to extract all the possible stylistic information encoded in the
documents (emoticons, character flooding, capital letters, references to other
users, hyperlinks, hashtags, etc.). Experiments with SVM showed up to 90% of
performance.
