There have been many attempts at automatically recognising author personality traits from text, typically incorporating linguistic features with conventional machine learning models, e.g. linear regression or Support Vector Machines. In this work, we propose to use deep-learning-based models with atomic features of text – the characters – to build hierarchical, vectorial word and sentence representations for the task of trait inference. On a corpus of tweets, this method shows state-of-the-art performance across five traits and three languages (English, Spanish and Italian) compared with prior work in author profiling. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.