Multimodal Detection of Information Disorder from Social Media

TL;DR

This paper introduces a multimodal network architecture for detecting fake news on social media by integrating textual, visual, user comments, and metadata, demonstrating the effectiveness of multimodal fusion.

Contribution

It presents a novel multimodal fusion approach that combines multiple content and context modalities for large-scale fake news detection.

Findings

Multimodal analysis significantly improves fake news detection accuracy.

All modalities contribute positively when fused properly.

The proposed method outperforms unimodal approaches.

Abstract

Social media is accompanied by an increasing proportion of content that provides fake information or misleading content, known as information disorder. In this paper, we study the problem of multimodal fake news detection on a largescale multimodal dataset. We propose a multimodal network architecture that enables different levels and types of information fusion. In addition to the textual and visual content of a posting, we further leverage secondary information, i.e. user comments and metadata. We fuse information at multiple levels to account for the specific intrinsic structure of the modalities. Our results show that multimodal analysis is highly effective for the task and all modalities contribute positively when fused properly.