A Comprehensive Review of Fake News Detection Using Linguistic Features, Word Embeddings, and Deep Learning: A Proposed Hybrid Multi-Signal Framework

Abstract

The rapid proliferation of fake news across social media and messaging platforms poses a serious threat to information integrity, public discourse, and institutional trust. Automated detection research has progressed through linguistic feature-based methods, recurrent and attention-based neural architectures, word-embedding strategies, and, increasingly, hybrid systems that fuse multiple complementary signals. This paper presents a comprehensive review of 30 studies spanning foundational linguistic-cue research, classical machine learning, RNN/LSTM/Bi-LSTM architectures, transformer-augmented models, propagation- and stance-based methods, adversarial robustness, and explainability-oriented approaches. We organize these works into a structured taxonomy, compare them across accuracy, interpretability, computational cost, and real-time suitability, and synthesize ten recurring research gaps: limited real-time readiness, poor explainability, weak performance on short informal text, fragmented multi-signal integration, vulnerability to sophisticated fake content, high computational cost, weak crossdomain generalization, an unresolved accuracy/efficiency/interpretability trade-off, the absence of a principled safeguard against ensemble override of factual contradictions, and lack of resilience to external verification-service failure. Building on this synthesis, we formulate a precise problem statement and propose a hybrid multi-signal methodology that integrates heuristic linguistic analysis, Bi-LSTM-based contextual modelling, real-time factual verification with deterministic offline fallback, and a decision safeguard mechanism (Veto Logic) within an explainable decision framework. A mathematical formulation including the override condition, a fusion model, and an algorithmic procedure for the proposed framework are presented, providing the complete conceptual and methodological foundation for an experimentally validated hybrid detection system — TruthLens — reported in our companion result paper.