The rapid proliferation of fake news across digital platforms has emerged as a challenging task, undermining public discourse, and compromising public trust in media. Initially, the detection efforts focused on textual features using traditional machine learning algorithms, which, despite their effectiveness, were limited by the manual and time-consuming process of feature extraction. The advent of deep learning heralded a significant shift, with Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) offering enhanced capabilities in capturing the nuanced interplay of textual elements. Parallelly, the examination of visual features through multimodal methods demonstrated the importance of incorporating images and videos, further refined by Graph Convolutional Networks (GCNs) and attention mechanisms for superior accuracy. However, challenges persist in integrating and fully utilizing multimodal information, particularly in addressing the limitations of deep versus shallow feature analysis and the adaptability of models across diverse scenarios. This paper synthesizes the methodologies, findings, and critical evaluations of these approaches, highlighting the advancements and identifying areas for future research in the detection of fake news.

Fake News Detection, Textual Feature Extraction, Visual Feature Analysis, Multimodal Analysis, Machine Learning Algorithms, Deep Learning

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