Abstract
This paper describes our proposed method for the author profiling task at PAN 2019. The aim of this task is to identify the type of a Twitter user (i.e. bot or human). Then, in case of a human, determine its gender (i.e. male or female). Our approach uses a set of language independent features and it applies machine learning algorithms. After an in-depth experimental study, conducted on English and Spanish datasets, we show that by using a simple set of stylistic information, we can surpass other existing methods that mainly depend on the content of the tweets. For the English dataset, accuracies of 93.06% and 90.04% are obtained for bot an gender classification tasks respectively. Using Spanish tweets, accuracies of 90.53% and 89.11% are achieved for bot and gender detection task respectively.