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

Efficient Social Network Multilingual Classification using Character, POS n-grams and Dynamic Normalization