  This paper presents a computational approach to author profiling taking
gender and language variety into account. We apply an ensemble system with the
output of multiple linear SVM classifiers trained on character and word
$n$-grams. We evaluate the system using the dataset provided by the organizers
of the 2017 PAN lab on author profiling. Our approach achieved 75% average
accuracy on gender identification on tweets written in four languages and 97%
accuracy on language variety identification for Portuguese.
