Abstract
Today, detecting fake news has become challenging as anyone can interact by freely sending or receiving electronic information. Deep learning processes to detect multimodal fake news have achieved great success. However, these methods easily fuse information from different modality sources, such as concatenation and element-wise product, without considering how each modality affects the other, resulting in low accuracy. This study presents a focused survey on the use of deep learning approaches to detect multimodal visual and textual fake news on various social networks from 2019 to 2024. Several relevant factors are discussed, including a) the detection stage, which involves deep learning algorithms, b) methods for analyzing various data types, and c) choosing the best fusion mechanism to combine multiple data sources. This study delves into the existing constraints of previous studies to provide future tips for addressing open challenges and problems.
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