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