Author Profiling: Bot and Gender Prediction using a Multi-Aspect Ensemble Approach Notebook for PAN at CLEF 2019 Hamed Babaei Giglou 1 , Mostafa Rahgouy 1* , Taher Rahgooy 2 , Mohammad Karami Sheykhlan 1 ,and Erfan Mohammadzadeh 1 1 University of Mohaghegh Ardabili, Computer Science Department, Ardabil, Iran * mostafarahgouy@student.uma.ac.ir {hamedbabaeigiglou, mohammadkaramisheykhlan, er.mohammadzadeh}@gmail.com 2 Tulane University , Computer Science Department, New Orleans, LA, USA trahgooy@tulane.edu Abstract Author Profiling is one of the most important tasks in authorship anal- ysis. In PAN 2019 shared tasks, the gender identification of the author is the main focus. Compared to the previous year the author profiling task is expended by having documents written by bots. In order to tackle this new challenge we pro- pose a two phase approach. In the first phase we exploit the TF-IDF features of the documents to train a model that learns to detect documents generated by bots. Next, we train three models on character-level and word-level representations of the documents and aggregate their results using majority voting. Finally, we empirically show the effectiveness of our proposed approach on the PAN 2019 development dataset for author profiling. Keywords: Author Profiling, User Modeling, Natural Language Processing, Su- pervised Machine Learning, Stacking ensemble. 1 Introduction As computational power grows and artificial intelligence techniques evolve, new chal- lenges, that were out of reach of machine learning in few years ago, emerge. One such a problem is author profiling. Different from the traditional authorship identification, in which a closed set of possible authors is known, author profiling aims to determine what are the characteristics of the authors: their age, gender, native language among others[13]. Copyright c  2019 for this paper by its authors. Use permitted under Creative Commons Li- cense Attribution 4.0 International (CC BY 4.0). CLEF 2019, 9-12 September 2019, Lugano, Switzerland.