Fake news articles often stir the readers' attention by means of emotional\nappeals that arouse their feelings. Unlike in short news texts, authors of\nlonger articles can exploit such affective factors to manipulate readers by\nadding exaggerations or fabricating events, in order to affect the readers'\nemotions. To capture this, we propose in this paper to model the flow of\naffective information in fake news articles using a neural architecture. The\nproposed model, FakeFlow, learns this flow by combining topic and affective\ninformation extracted from text. We evaluate the model's performance with\nseveral experiments on four real-world datasets. The results show that FakeFlow\nachieves superior results when compared against state-of-the-art methods, thus\nconfirming the importance of capturing the flow of the affective information in\nnews articles.\n