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Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
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 bydoi:10.18653/v1/2021.eacl-main.56 fatcat:5eo7q7nbarf5rawam4nvdrbdai