Fake news on social networks is a challenging problem due to the rapid dissemination and volume of information, as well as the ease of creating and sharing content anonymously. Fake news stories are problematic not only for the credibility of online journalism, but also due to their detrimental real-world consequences. The primary research objective of this study is: What are the recent state-of-the-art modalities based on deep learning to detect fake news in social networks. This paper presents a systematic literature review of deep learning-based fake news detection models in social networks. The methodology followed a rigorous approach, including predefined criteria for study selection of deep learning modalities. This study focuses on the types of deep learning modalities; unimodal (refers to the use of a single model for analysis or modeling purposes) and multimodal models (refers to the integration of multiple models). The results of this review reveal the strengths and weaknesses of modalities approaches, as well as the limitations of low-resource languages datasets. Furthermore, it provides insights into future directions for deep learning models and different fact checking techniques. At the end of this study, we discuss the problem of fake news detection in the era of large language models in terms of advantages, drawbacks, and challenges.

中文翻译：

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社交网络假新闻检测的模态深度学习框架：系统文献综述


社交网络上的假新闻是一个具有挑战性的问题，因为它的信息传播迅速、数量庞大，而且很容易匿名创建和分享内容。假新闻报道不仅对在线新闻的可信度造成问题，还因为它们对现实世界的有害后果。本研究的主要研究目标是：最近基于深度学习的检测社交网络中假新闻的最新模式是什么。本文对社交网络中基于深度学习的假新闻检测模型进行了系统的文献综述。该方法遵循严格的方法，包括深度学习模式研究选择的预定义标准。本研究侧重于深度学习模式的类型;单峰模型（指使用单个模型进行分析或建模目的）和多模态模型（指多个模型的集成）。本综述的结果揭示了模态方法的优缺点，以及低资源语言数据集的局限性。此外，它还提供了对深度学习模型和不同事实核查技术的未来方向的见解。在本研究的最后，我们从优缺点和挑战的角度讨论了大型语言模型时代假新闻检测的问题。