Traditional event models underlying naive Bayes classifiers assume probability distributions that are not appropriate for binary data generated by human behaviour. In this work, we develop a new event model, based on a somewhat forgotten distribution created by Kenneth Ted Wallenius in 1963. We show that it achieves superior performance using less data on a collection of Facebook datasets, where the task is to predict personality traits, based on likes.