Linguistic Feature Extraction and Classification for Online Fake News Detection

The proliferation of deepfakes, misinformation, disinformation, and broader post-truth narratives—collectively characterized as fake news—has generated significant concern regarding the influence of digital platforms on public discourse and democratic stability. The widespread diffusion of manipulated multimedia content and algorithmically amplified false narratives across social media ecosystems has intensified challenges related to information credibility, media literacy, and institutional trust. This project addresses the growing threat of digital deception by developing a computational framework for automated fake news classification using advanced Natural Language Processing (NLP) methodologies. The proposed approach leverages linguistic feature extraction, semantic embeddings, sentiment analysis, contextual modeling, and transformer-based deep learning architectures to differentiate between authentic and fabricated news content. Supervised machine learning models are trained on benchmark datasets to capture syntactic irregularities, rhetorical patterns, source credibility signals, and discourse-level inconsistencies commonly associated with deceptive narratives. In parallel, the study explores the integration of Blockchain and other Distributed Ledger Technologies (DLTs) as complementary mechanisms to enhance data provenance, traceability, and content integrity within digital information ecosystems. By utilizing cryptographic hashing, decentralized consensus protocols, and immutable ledger recording, blockchain-based systems can establish verifiable timestamps, authenticate content origins, and prevent unauthorized post-publication alterations. Such architectures enable transparent audit trails and peer-to-peer verification processes, thereby strengthening trust in news dissemination networks. The research provides a systematic review of existing DLT-based interventions for combating misinformation, including decentralized content verification platforms, reputation-based trust scoring systems, and tokenized incentive models for fact-checking communities. Additionally, it identifies critical open challenges such as scalability limitations, interoperability constraints, privacy-preserving identity management, adversarial attacks on NLP models, and the ethical implications of automated content moderation. By synthesizing advances in NLP-driven content analysis and blockchain-enabled integrity assurance, this study proposes a hybrid resilience framework aimed at mitigating cyber-enabled misinformation threats. The findings offer strategic recommendations for future interdisciplinary research, emphasizing robust model generalization, explainable AI techniques, cross-platform collaboration, and regulatory alignment. Collectively, these efforts contribute toward enhancing information reliability, reinforcing democratic resilience, and fostering trust in contemporary digital media ecosystems.