Twitter Text and Image Gender Classification with a Logistic Regression N-Gram Model: Notebook for PAN at CLEF 2018

We present our participation in the PAN 2018 Author Profiling shared task, classifying authors on gender for English, Arabic and Spanish. We participated in all sub-tasks and propose a system for classification with text, images and the combination of those two. Our final submitted system is a Logistic Regression classifier that uses word and character n-grams as textual features and a set of automatically derived image-based features such as the presence, proportion and number of faces to detect selfies as well as the faces’ emotions and gender. We experimented with word embeddings, which negatively affected our system’s performance. Our cross-validated training results shows slight improvements in performance for Arabic and Spanish when image-based features are added to text-based features. Our highest scores on the PAN 2018 test dataset are accuracies of 81.2% for English using only text-based features, 78.7% for Arabic using both textand image-based features and 80.3% for Spanish using only text-based features. Overall, we finished 6 in the global ranking with an average accuracy for our text and image combination system of 79.6%.