Genesis Point Detection: Identifying and Neutralizing Disinformation at its Origin

The growth of fake news content on social media websites poses an imminent danger to the stability of society, its views and democracy. Although modern disinformation is a multifaceted problem that needs a more sophisticated solution, the groundwork has been laid by traditional machine learning methods. The present paper summarises a decade of research to develop an all-encompassing framework of fake news detection. We provide a literature review of the developments that uncovered content-based and feature-engineering techniques to the more advanced deep learning systems that incorporate textual, visual, and social contexts. The proposed system, the Shallow-Deep Cross-modal Verifier (SD-CMV), leverages a hybrid methodology combining a pre-trained language model for deep semantic analysis with a shallow, wide model for hand-crafted feature extraction, such as user profiles and propagation patterns. These are fused with a visual authenticity analyser to create a robust multimodal classifier. A novel aspect of our work is the incorporation of a Temporal Propagation Module using a Recurrent-Convolutional network to classify news virality paths for early detection. The Results from a conceptual implementation on a synthesised multimodal dataset demonstrate the superiority of this integrated approach over unimodal baselines. This paper focuses on the future of effective deception detection lies in synergistic models that are content-aware, context-sensitive, and temporally adaptive.