Jun 2026· International Journal for Research in Applied Science and Engineering Technology· 0 citations
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.
The rapid spread of misinformation across digital platforms has made fake news detection a critical challenge, as it
can influence public opinion, disrupt social stability, and reduce trust in credible information sources. Manual verification is no
longer feasible at scale due to the large volume of content generated daily. Existing approaches have explored hybrid
architectures combining transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT)
with sequential models like Long Short-Term Memory (LSTM) for fake news classification; however, such approaches may have
limitations in capturing the diverse linguistic, contextual, and structural patterns present in textual data. To address this
limitation, this paper proposes a hybrid transformer-based ensemble model for automated fake news identification using the
FakeNewsNet dataset. The proposed system integrates BERT with LSTM for contextual and sequential learning, Robustly
Optimized BERT Pretraining Approach (RoBERTa) for improved textual representation, and Light Gradient Boosting Machine
(LightGBM) for learning statistical patterns from textual features. Individual models provide strong baseline performance, while
the ensemble combines their predictions using a weighted strategy to improve overall accuracy and robustness. Experimental
results show that the ensemble model achieves an accuracy of approximately 93%, outperforming the individual constituent
models. The system can be applied in real-time news verification platforms to assist users, journalists, and fact-checkers in
identifying misleading information more effectively
M. E. Babu, G. Sukanya· International Journal for Re...· 0 citations
The rapid growth of online media has accelerated the circulation of misinformation, posing serious risks to public trust, social stability, and democratic processes. Detecting fake news remains a challenging task, particularly because traditional machine learning approaches such as Support Vector Machines and Naïve Bayes rely on shallow linguistic cues and often fail to recognize sarcasm, implicit tone, or evolving writing styles. Earlier deep learning models, including CNNs and LSTMs, provide modest improvements but continue to struggle with long text and shifting topical contexts. 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. To enhance generalization, news articles from ten publicly available datasets are consolidated into a single corpus namely FakeDiverse, that exposes the models to a broader spectrum of linguistic patterns. MinHash similarity along with Locality Sensitive Hashing algorithm has been employed to evaluate the lexical redundancy within the FakeDiverse corpus as a post hoc analysis. Class imbalance between real and fake news is addressed through a weighted cross-entropy loss function, ensuring that minority-class instances are adequately represented during training. FakeDiverse dataset is split in the ratio of 80:20 for training and testing, and subsequently the models BERT and DeBERTA are trained on the training set and evaluated using the test sets. Experimental results demonstrate that both the models capture contextual nuances effectively, with BERT achieving 98% accuracy and DeBERTa reaching 99%. The findings demonstrate that BERT and DeBERTa achieves strong performance on FakeDiverse corpus, and cross-dataset evaluation shows that models have challenges to generalize unseen data distribution thereby highlighting 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
ABSTRACT
The rapid proliferation of fake news and misinformation across digital platforms poses a serious threat to democratic processes, public health, and social stability. This research paper presents a comprehensive study of an AI-based fake news detection system leveraging Natural Language Processing (NLP) techniques and multiple machine learning algorithms. The proposed system analyzes textual content, linguistic patterns, writing style, and sentiment cues to automatically classify news articles as real or fake.
Several classification algorithms — including Naive Bayes, Logistic Regression, Support Vector Machine (SVM), Random Forest, Decision Tree, and Long Short-Term Memory (LSTM) neural networks — are implemented and compared. Feature extraction is performed using TF-IDF and Bag of Words representations. The system is evaluated using standard metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.
Experimental results demonstrate that deep learning models, particularly LSTM, achieve superior contextual understanding, while traditional models offer faster inference. The paper also discusses current challenges such as sarcasm detection, multilingual content, and computational constraints, along with future directions including real-time monitoring and transformer-based architectures. This work contributes significantly to the broader effort of combating misinformation through technological innovation and provides a comprehensive roadmap for future research in this critical area.
Keywords: Fake News Detection, Natural Language Processing, Machine Learning, Deep Learning, LSTM, TF-IDF, Text Classification, Misinformation, Social Media
Shahid Khan, Dr. Abdul Majid Farooqi· International Scientific Jou...· 0 citations
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.
Ardra P Namboodiri, Archa P S, Honey Mol O· 2026 6th International Confe...· 0 citations
The rapid proliferation of user-generated content on social media platforms has transformed the way information is created, shared, and consumed. While this democratization of information has clear benefits, it has also enabled the large-scale dissemination of fake news, which poses serious threats to public health, political stability, financial markets, and social cohesion. Automatic fake news detection has therefore become an active and urgent research problem in computer science. This paper proposes a multi-model learning framework that combines the complementary strengths of classical machine learning classifiers, deep sequential neural networks, and transformer-based contextual language models to detect fake news on social media. The framework integrates lexical, semantic, and contextual features through a weighted soft-voting ensemble that fuses predictions from a Support Vector Machine, a Bidirectional Long Short-Term Memory network, and a fine-tuned BERT encoder. Experiments were conducted on three widely used public benchmark datasets, namely LIAR, FakeNewsNet, and ISOT, comprising more than one hundred thousand labeled news statements and articles. The proposed ensemble achieved an accuracy of 96.4% and an F1-score of 96.1% on the ISOT dataset, outperforming each individual constituent model and several competitive baselines reported in the literature. Ablation studies confirm that the transformer component contributes the largest performance gain, while the ensemble fusion improves robustness and reduces variance across datasets. The results demonstrate that combining heterogeneous learning paradigms yields a more accurate and generalizable fake news detector than any single model in isolation. The paper also discusses computational cost, interpretability, and limitations, and outlines directions for multimodal and cross-lingual extensions.
Mrs Priyal Verma· International Journal of Res...· 0 citations
The rapid growth of social media and online news platforms has significantly increased the spread of misinformation
and fake news. Fake news can influence public opinion, create social unrest, and negatively impact decision-making
processes. Therefore, developing intelligent systems capable of automatically identifying fake news has become an important
research area in Artificial Intelligence and Natural Language Processing (NLP). The proposed work presents a Fake News
Detection System that combines traditional machine learning techniques with Large Language Model (LLM) embeddings
to improve classification performance. The proposed system utilizes textual news data from benchmark datasets such as
WELFake and LIAR. Initially, the news articles undergo pre-processing steps including text cleaning, normalization, and
removal of irrelevant characters. Two different feature extraction approaches are employed: Term Frequency-Inverse
Document Frequency (TF-IDF) and contextual embeddings generated using the pre-trained BERT (Bidirectional Encoder
Representations from Transformers) model. The extracted features are used to train and evaluate multiple Machine learning
classifiers, including Logistic Regression, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and XGBoost.
Performance evaluation is carried out using standard metrics such as Accuracy, Precision, Recall, F1-Score, and Area Under
the ROC Curve (AUC-ROC). Comparative analysis is performed to assess the effectiveness of traditional TF-IDF features
versus BERT-based semantic embeddings. Experimental results demonstrate that BERT embeddings capture contextual
and semantic information more effectively than conventional text representations, leading to improved fake news
classification accuracy. The system also provides visualization tools such as confusion matrices, ROC curves, and
performance comparison charts for comprehensive evaluation. The proposed framework highlights the potential of
integrating transformer-based language models with classical machine learning algorithms to build robust and scalable fake
news detection systems. Our work contributes to combating misinformation by providing an efficient and automated
approach for identifying deceptive news content in digital media environments.
Umme Noor Us Saqa, Sreenivasa B. R.· International Journal of Inn...· 0 citations