Fake textual and image news detection on social media using natural language processing

A rising amount of content on social media involves inaccurate and misleading information, a phenomenon known as knowledge disorder. In this study, we look into multimodal false news identification using a large-scale multimodal dataset. We propose a multifunctional network architecture capable of supporting different types and levels of information fusion. In addition to the visual and written contents of a posting, we leverage secondary information of users’ comments and metadata. We mix information at different levels to compensate for the modality’s distinct inherent structure. Our findings demonstrate that multidimensional analysis is highly successful for the job and that, when appropriately fused, all modalities contribute favourably. Automatic machine learning categorization models are useful in counteracting the broad dissemination of fake news. However, lacking a suitable, comprehensive dataset is challenging for fake news study and model building. Previous fake news datasets needed more scope and breadth of our collection, which contains multimodal text and picture data, information, comments data, and fine-grained false news categorization. We presented a text-and-image-based architecture model for identifying fake information.