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        <title>FakeFlow: Fake News Detection by Modeling the Flow of Affective Information</title>
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        <namePart type="given">Bilal</namePart>
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    <name type="personal">
        <namePart type="given">Francisco</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.eacl-main.56</identifier>
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%0 Conference Proceedings
%T FakeFlow: Fake News Detection by Modeling the Flow of Affective Information
%A Ghanem, Bilal
%A Ponzetto, Simone Paolo
%A Rosso, Paolo
%A Rangel, Francisco
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F ghanem-etal-2021-fakeflow
%X 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.
%R 10.18653/v1/2021.eacl-main.56
%U https://aclanthology.org/2021.eacl-main.56
%U https://doi.org/10.18653/v1/2021.eacl-main.56
%P 679-689

Markdown (Informal)

[FakeFlow: Fake News Detection by Modeling the Flow of Affective Information](https://aclanthology.org/2021.eacl-main.56) (Ghanem et al., EACL 2021)

• FakeFlow: Fake News Detection by Modeling the Flow of Affective Information (Ghanem et al., EACL 2021)

ACL

• Bilal Ghanem, Simone Paolo Ponzetto, Paolo Rosso, and Francisco Rangel. 2021. FakeFlow: Fake News Detection by Modeling the Flow of Affective Information. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 679–689, Online. Association for Computational Linguistics.