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

TL;DR

FakeFlow is a neural model that detects fake news by analyzing how emotional and topical information flows within articles, outperforming existing methods on multiple datasets.

Contribution

This paper introduces FakeFlow, a novel neural architecture that models the flow of affective information in news articles for improved fake news detection.

Findings

FakeFlow outperforms state-of-the-art methods on four datasets.

Modeling affective information flow enhances fake news detection accuracy.

Affective flow modeling is crucial for understanding manipulative news articles.

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.