KeepUp: A unified framework fusing knowledge extraction, social platform engagement, and user profiling for fake news detection

Approximately half of the global population relies on social media platforms such as Facebook, Twitter, and Instagram for news consumption. The vast volume and rapid dissemination of information on these platforms pose substantial challenges for the timely and accurate detection of fake news. Academics are working harder to develop AI-based automated systems to check news accuracy because of the detrimental effects of misinformation on public health, social trust, and political stability. But the majority of false news detection methods currently in use focus primarily on content-based features, often ignoring essential factors such as user profiling, social context, and knowledge extraction. The knowledge-based features necessary for effective document retrieval, position identification, social engagement analysis, and user profile integration are often absent from datasets, even though some of them contain elements of social context and user behavior. This work offers a thorough, fully annotated dataset that integrates user profiles, stance information, social engagements, knowledge extraction, and content elements into a single resource to overcome these limitations. Building on this dataset, this study creates KeepUp, a unified system that integrates user profiles, social media activity, and knowledge extraction to detect bogus news. KeepUp outperforms all baseline models, achieving a detection accuracy of 0.78, demonstrating the effectiveness of this combined approach.