FakeFlow: Fake News Detection by Modeling the Flow of Affective Information (2021.eacl-main)
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| Challenge: | In short news articles, authors add exaggerations or fabricate events to manipulate readers' emotions. |
| Approach: | They propose to model the flow of affective information in fake news articles using a neural architecture and combine topic and affective data extracted from text. |
| Outcome: | The proposed model outperforms state-of-the-art methods on four real-world datasets and shows that it can capture the flow of affective information in fake news articles. |
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