Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 1421-1426· 0 citations· 17 references
Abstract
The rapid proliferation of digitally distributed news has made large-scale automated verification an urgent research priority, as deceptive content spreads across social platforms faster than manual reviewers can evaluate it. This work introduces a two-stage deep learning pipeline in which a pretrained BERT encoder generates rich contextual token embeddings that are subsequently processed by a Bidirectional Long Short-Term Memory (BiLSTM) network, enabling the classifier to exploit both sentence-level semantics and document-level narrative flow simultaneously. Experiments on the WELFake benchmark [1] following a standardised cleaning pipeline—lowercasing, URL stripping, tokenisation, stop-word removal, and WordNet lemmatisation— yielded 98.7 % accuracy and an F1-score of 0.986. This surpasses a BERT-only baseline by 3.1 percentage points and exceeds several previously published state-of-the-art results. The gains confirm that coupling transformer-based semantic representations with recurrent sequential modelling produces a measurable and reproducible improvement in misinformation detection. Planned extensions include multilingual evaluation and knowledge-distilled encoder replacements targeting real-time throughput constraints.
A hybrid transformer-based ensemble model for automated fake news identification using the FakeNewsNet dataset is proposed and Experimental results show that the ensemble model achieves an accuracy of approximately 93%, outperforming the individual constituent models.
M. E. Babu, G. Sukanya· International Journal for Re...· 0 citations
A robust preprocessing pipeline incorporating Google Translation, spaCy-based NER with hyphenated-word normalization, and a future-event-aware fallback logic is proposed, achieving a 40 percent reduction in inference latency and closing the linguistic generalization gap left by previous works.
Rishabh Kumar, Aditya Kumar· Journal of Frontiers in Mult...· 0 citations
This study examines the effectiveness of two transformer-based architectures—BERT and DeBERTa—for identifying fake news using only textual information from headlines and article bodies and achieves strong performance on FakeDiverse corpus, demonstrating the need for enhanced generalization strategies as well as domain adaptation.
Archana Praveen Kumar, A. S, Akshara G. Bhat et al.· Scientific Reports· 0 citations
DAR-Lite is proposed, a serial two-stage framework that rethinks the detection pipeline through explicit decoupling of representation denoising and contextual reasoning, and achieves a favorable balance between detection performance and computational cost.
Maolin Wang, Ziting Mai, Zichun Liu et al.· Proceedings of the 32nd ACM...· 0 citations
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.
Zafeer Ahmed Patel, Ashwini S. Gaikwad· International Journal for Re...· 0 citations
Results indicate that the proposed architecture successfully combines the process of refining the semantic features of text data while providing an explainable artificial intelligence solution for real-world applications of fake news detection.
Sudha Patel, Shilpa Serasiya, Sachi Bhavsar et al.· International journal of com...· 0 citations