A Review of Deep Learning Techniques for Multimodal Fake News and Harmful Languages Detection

Abstract

The detection of fake news and harmful languages has become increasingly important in today's digital age. As the prevalence of fake news and harmful languages continue to increase, so also is the correspondent negative impact on individuals and the society. Researchers are exploring new techniques to identify and combat these issues. Deep neural network (DNN) has found a wide range of applications in diverse problem domains including but not limited to fake news and harmful languages detection. Fake news and harmful languages are currently increasing online and the mode of dissemination of these contents is fast changing from the traditional unimodal to multiple data forms including texts, audios, images and videos. Multimedia contents containing fake news and harmful languages pose more complex challenges than unimodal contents. The choice and efficacy of the fusion methods of the multimedia contents is one of the most challenging. Our area of focus is multimodal techniques based on deep learning that combines diverse data forms to improve detection accuracy. In this review, we delve into the current state of research, the evolution of deep learning techniques that have been proposed for multimodal fake news and harmful languages detection and the state-of-the-art (SOTA) multimedia data fusion methods. In all cases, we discuss the prospects, relationships, breakthroughs and challenges.