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Fake news articles often stir the readers' attention by means of emotionalappeals that arouse their feelings. Unlike in short news texts, authors oflonger articles can exploit such affective factors to manipulate readers byadding exaggerations or fabricating events, in order to affect the readers'emotions. To capture this, we propose in this paper to model the flow ofaffective information in fake news articles using a neural architecture. Theproposed model, FakeFlow, learns this flow by combining topic and affectiveinformation extracted from text. We evaluate the model's performance withseveral experiments on four real-world datasets. The results show that FakeFlowachieves superior results when compared against state-of-the-art methods, thusconfirming the importance of capturing the flow of the affective information innews articles.

FakeFlow: Fake News Detection by Modeling the Flow of Affective Information