I-KAHAN: Image-Enhanced Knowledge-Aware Hierarchical Attention Network for Multi-modal Fake News Detection

Abstract

In the quest to combat the proliferation of fake news, accurate detection of fabricated news content has become increasingly desirable. While existing methodologies leverage a variety of news attributes, such as text content and social media comments, few incorporate diverse features from different modalities like images. In this paper, Image-Enhanced Knowledge-Aware Hierarchical Attention Network (I-KAHAN) architecture is proposed as an enhancement to the existing KAHAN architecture. The I-KAHAN architecture utilizes a wide variety of attributes including news content, user comments, external knowledge, and temporal information which are inherited from the KAHAN architecture, and extends it by integrating image-based information as an additional feature. This work contributes to refining and expanding fake news detection methodologies by embracing a more comprehensive range of features and modalities, and offers valuable insights into the effectiveness of various methods for the numerical representation of images, feature aggregation and dimensionality reduction. Experiments conducted on two real-world datasets, PolitiFact and GossipCop, assessing the performance of the I-KAHAN architecture, demonstrated approximately 3% improvement in accuracy over the KAHAN architecture, highlighting the potential benefits of incorporating diverse features and modalities for enhanced fake news detection performance.