FakeFlow: Fake News Detection by Modeling the Flow of Affective Information

Anonymous

TL;DR: Fake News Articles detection by Modeling the Flow of Affective Information

Abstract:

Unlike in short news texts, authors of longer articles stir the readers’ attention by means of emotional appeals that
arouse their feelings. To capture this, Bilal et al. (2021) propose in their paper to model the flow of affective information
in fake news articles using a neural architecture. The authors claim to introduce a model, FakeFlow for learning flow of
affective information in fake news articles that outperforms the state-of-the-art methods for this task.

Paper Url: https://aclanthology.org/2021.eacl-main.56/

Paper Venue: ACL 2021

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/fakeflow-fake-news-detection-by-modeling-the/code)