As demonstrated in previous studies, Alzheimer’s diseaseleads to a degradation of vocabulary and communication skills.Novels by writers who are known to have suffered from thisdisease were compared with respect to their lexical richnessand syntactic complexity. Those written after the break-outof the disease have shown to use a considerably smaller lex-icon and a reduced syntactic complexity of the sentences.This makes us assume that writings of individual authors canbe classified automatically into “pre-Alzheimer’s period” and“Alzheimer’s period”. But the writing style of an author ishighly individual. Can we still detect whether any given novelis written by an author who suffers from Alzheimer’s? To as-sess this, we use a corpus of novels by three well-known writ-ers who were diagnosed with Alzheimer’s: Iris Murdoch, TerryPratchett and Agatha Christie. Using a mostly stylistic set offeatures we are able to distinguish between novels written un-der the influence of the disease and novels written by healthywriters with more than 82% accuracy. The classification ofthe novels of a given author into “pre-Alzheimer’s period” and“Alzheimer’s period” is accomplished with more than 86% ac-curacy. We also prove that our feature set is versatile enoughto be able to distinguish between authors in general and bookswith high precision.

In data-driven gender identification, it has been so far largely assumed that the same types of (mostly content-oriented) data features can be used to differentiate between male and female authors. In most cases, this distinction is done in a monolingual scenario. In this work, we discuss a set of features that distinguish between genders in six different datasets of blog data in English, Spanish, French, German, Italian and Catalan with accuracies that range from 77% to 88%. Using a reduced set of language-independent structural features in a multilingual scenario we first identify the gender and then the gender and language of the author, achieving accuracies higher than 74%.