An Analysis of Cross-Genre and In-Genre Performance for Author Profiling in Social Media

User profiling on social media data is normally done within a supervised setting. A typical feature of supervised models that are trained on data from a specific genre, is their limited portability to other genres. Cross-genre models were developed in the context of PAN 2016, where systems were trained on tweets, and tested on other non-tweet social media data. Did the model that achieved best results at this task got lucky or was it truly designed in a cross-genre manner, with features general enoughto capture demographics beyond Twitter? We explore this question via a series of in-genre and cross-genre experiments on English and Spanish using the best performing system at PAN 2016, and discover that portability is successful to a certain extent, provided that the sub-genres involved are close enough. In such cases, it is also more beneficial to do cross-genre than in-genre modelling if the cross-genre setting can benefit from largeramounts of training data than those available in-genre