"HYBRID DEEP LEARNING FRAMEWORKS FOR FAKE NEWS DETECTION:A MULTIMODAL AND EXPLAINABLE AI APPROACH"

Abstract

The growth of social media platforms changed how people access and transmit information. Although the digital age allows users unprecedented access to updates and news from around the world, it also provides a platform for the spread of  fake news, a type of content intended to misguide users and provide purposeful misinformation. The ramifications of consequences of  viral fake news can result in societal and public unrest, political deception, and economic destabilization. Outdated methods of data verification, though still useful, are primarily manual and slow and fail to keep up with the growing volume of data to be validated each day.

To address these specific challenges, scholars have begun utilizing deep learning techniques for automatic fake news detection and classification. Deep learning architectures, especially Convolutional Neural Networks, Recurrent Neural Networks, and, more recently, Transformer architectures like BERT, excel at capturing and contextualizing semantic and relational information in text. Furthermore, multimodal approaches that integrate text, images, and metadata improve performance because they use information not just contained in the text of an article.

The purpose of this research paper is examining the extent to which various deep learning techniques are used to detect fake news. It discusses the proposed deep learning models before suggesting an advanced hybrid framework to improve detection accuracy and assesses it against state-of-the-art real-world datasets. The research also analyzes the challenges of interpretability and ethics, as well as the practical implications and challenges of large-scale deployment. The purpose of the research is to assist the design of intelligent systems which attempt to reduce the damaging effects of misinformation during the digital age.