DeepTrustChain: A Blockchain-Anchored Hybrid Deep Learning Framework for High-Integrity Fake News Detection

Abstract

The uncontrolled propagation of fake news on the digital frontiers continues to undermine the confidence of the populace, disorganizing supply chains and misled decision making on high stake regions. The existing fake news detectors are all largely text-based and are driven by machine learning or deep learning and do not pay much focus to integrity, provenance, and post-hoc verifiability of the model predictions. Moreover, the current procedures are usually trained and tested in controlled settings and do not facilitate adversarial manipulation of content and metadata, and are rarely provided to produce audit trails which cannot be modified by auditors of identified artifacts. It is in this respect that this paper introduces DeepTrustChain, a blockchain anchored hybrid deep learning framework in the detection of high-integrity fake news. The proposed system uses an encoder of text a transformer encoder with a Bidirectional recurrent module and an ensemble stacking layer to improve the robustness and generalization owing to the recent discoveries on stacking and ensemble methods of identifying fake news, and reliability-based methods of evaluation. At the same time, DeepTrustChain computes a trust score that represents a combination of model confidence and source level and propagation level features and anchors the prediction and proof on an authorized blockchain. Such a design provides verifiable, immutable records of the histories of the detection results and enables the decentralized checking of the news items by time. The conceptual evaluation of the framework in respect to multilingual and low resource states and can also leverage advancements in sequence modeling and representation learning in other applications such as music and affective computing. The key feature of DeepTrustChain is that it is an architecture that integrates integrity conscious hybrid deep learning with blockchain based anchoring, so that simultaneously achieves the high degree of detection accuracy, resistance to decision manipulation, and ultimate traceability of decision making regarding fake news.