Adaptive Interaction Fusion Networks for Fake News Detection

TL;DR

This paper introduces Adaptive Interaction Fusion Networks (AIFN), a novel approach for fake news detection that effectively models cross-interactions between posts and comments to improve detection accuracy.

Contribution

The paper proposes AIFN with GAIN and SFSN modules to capture semantic conflicts and enhance feature fusion, advancing fake news detection methods.

Findings

AIFN achieves state-of-the-art results on RumourEval and PHEME datasets.

AIFN improves detection accuracy by over 2%.

The model effectively captures semantic conflicts and correlations.

Abstract

The majority of existing methods for fake news detection universally focus on learning and fusing various features for detection. However, the learning of various features is independent, which leads to a lack of cross-interaction fusion between features on social media, especially between posts and comments. Generally, in fake news, there are emotional associations and semantic conflicts between posts and comments. How to represent and fuse the cross-interaction between both is a key challenge. In this paper, we propose Adaptive Interaction Fusion Networks (AIFN) to fulfill cross-interaction fusion among features for fake news detection. In AIFN, to discover semantic conflicts, we design gated adaptive interaction networks (GAIN) to capture adaptively similar semantics and conflicting semantics between posts and comments. To establish feature associations, we devise semantic-level fusion self-attention networks (SFSN) to enhance semantic correlations and fusion among features. Extensive experiments on two real-world datasets, i.e., RumourEval and PHEME, demonstrate that AIFN achieves the state-of-the-art performance and boosts accuracy by more than 2.05% and 1.90%, respectively.