In the context of the growing popularity of social media over the past ten years, an urgent problem of fake news spreading has arisen, which underscores the research’s relevance. The research aims to analyse current studies, focused on identifying and classifying fake news. To achieve this goal, a multimodal approach was chosen that combines deep learning frameworks and pre-trained models. This approach provides a comprehensive analysis of textual, visual, and audio information, allowing for more accurate identification of disinformation sources. The use of various knowledge transfer methods made it possible to process information efficiently, improving the quality of classification. The study conducted a thorough analysis of various data collection strategies, as well as a comparative analysis of available multimodal approaches to fake news detection and the datasets used. The results of this study included a detailed analysis of current research work in the field of fake news detection and the development of a multimodal approach to this problem. Pre-trained models and deep learning were used to process textual, visual, and audio information, which allowed for high accuracy in fake news detection. The results of the study showed that the multimodal approach allows for more accurate identification of sources of disinformation and increases the efficiency of fake news classification compared to other methods. A comparative analysis of different data collection strategies and datasets was also conducted, which helped to confirm the high efficiency of our approach in different conditions. The practical significance of the study is that it provides practical recommendations for the development and implementation of fake news detection systems based on multimodal approaches. Thus, this study not only reveals the actual problem of fake news but also provides practical tools for combating it, which is of significant importance for modern science and society. The study proposes a multimodal approach to accurately identify and classify fake news.
Key words: technologies, information environment, neural networks, testing approaches, disinformation sources.
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