Several studies have shown that the users of Twitter reveal their interests (i.e., what they like) while they share their opinions, preferences and personal stories. In this paper we describe Frisk a multilingual unsupervised approach for the categorization of the interests of Twitter users. Frisk models the tweets of a user and the interests (e.g., politics, sports) as bags of articles and categories of Wikipedia respectively, and ranks the interests by relevance, measured as the graph distance between the articles and the categories. To the best of our knowledge, existing unsupervised approaches do not address multilingualism and describe the users’ interests through bags of words (e.g., phone, apps), without a precise categorization (e.g., technology). We evaluated Frisk on a dataset including 1,347 users and more than three million tweets written in four different languages (English, French, Italian and Spanish). The results indicate that Frisk shows quantitative promise, also compared to approaches based on text classification (SVM, Naive Bayes and Random Forest) and LDA.