| Summary: | With the high-speed development of multimedia technologies, news content is very rich, including not only text but also image information. One of the most essential approaches for detecting fake news is through content analysis. Feature extraction and representation are the crucial steps of the task. How to accurately characterize news content is still a challenging problem. This article seeks to assist readers comprehend the various strategies connected with feature extraction and representation. Therefore, we scan various digital libraries to find all relevant papers published since 2010. This paper reviews methods that can extract and represent features from three perspectives: text, image, and multi-modal. In particular, we count the usage of these methods in various fake news detection tasks and detail the related theories. We hope that this review can promote the advancement of machine learning, neural networks, and other technologies so as to provide better services for fake news detection. ©2024 The authors.
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