Multi-Modal Approaches to Fake News Detection: Text, Image, and Video Analysis

Abstract

The proliferation of fake news has become a significant challenge in the digital age,
requiring advanced techniques for accurate detection. This paper investigates the use of multimodal approaches for fake news detection, integrating text, image, and video analysis. By
leveraging the complementary strengths of different types of data, we propose a comprehensive
framework that cross-references textual content with visual information and video context to
improve the accuracy and robustness of fake news detection. Our methodology involves the
development of machine learning models capable of analyzing and synthesizing insights from
multiple media types, including natural language processing (NLP) for text, computer vision for
images, and deep learning techniques for video analysis. The proposed system is evaluated on a
diverse dataset, demonstrating significant improvements in detection performance compared to
uni-modal approaches. This multi-modal framework represents a promising direction for
combating misinformation by providing a more holistic understanding of the content being
analyzed.