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The proliferation and rapid diffusion of fake news on the Internet highlight the need of automatic hoax detection systems. In the context of social networks, machine learning (ML) methods can be used for this purpose. Fake news detection strategies are traditionally either based on content analysis (i.e. analyzing the content of the news) or - more recently - on social context models, such as mapping the news' diffusion pattern., In this paper, we first propose a novel ML, fake news detection method which, by combining news content and social context features, outperforms existing methods in the literature, increasing their already high accuracy by up to 4.8%. Second, we implement our method within a Facebook Messenger chatbot and validate it with a real-world application, obtaining a fake news detection accuracy of 81.7%.

This work proposes a method based on the affinity propagation clustering technique to classify artists and find representative artists for each musical category ("musical world") using only the listening history log of a music service.Two variants of the proposed method are compared with a classic k-means clustering approach and an evaluation based on folksonomy analysis is provided. The results suggest that affinity propagation is highly effective in the music domain, allowing for better classification of artists than classic clustering techniques.Furthermore, an analysis of the results indicates that classifying music by genres, even using more than one genre for each artist, is sometimes an oversimplification of the dynamics that govern the music ecosystem. While most of the clusters found have a strict relationship with a music genre, the characterization of some of the emerged "musical worlds" is related to other aspects like the geographic origin of the artists, the prominent themes in the lyrics, the evocative potential and the association with a culture/lifestyle or the context in which the music has been used.