Author Profiling based on Text and Images: Notebook for PAN at CLEF 2018

In this paper we describe our participation in the PAN 2018 shared task of Author Profiling. In this task we identify the gender of authors based on written text and shared images. We describe our approaches to the text-based, imagebased and the combined task. The presence of three different languages raises the question whether a single model architecture can be built that works well on all three languages. We also propose a way to combine multiple predictions on shared content into a single prediction on user-level. Our final system for text is an ensemble of a Naive Bayes model and a RNN with attention. The image classification is done by finding selfies and predicting the gender of the person on those images using CNNs.

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