Aug 2026· International Journal of Engineering and Manufacturing· Vol 16, pp. 97-105· 0 citations
TL;DR
The proposed approach provides a simple and efficient solution for multilingual fake news detection in data-scarce environments with ensemble-based classifiers such as Random Forest and Gradient Boosting achieving reliable performance across both languages.
Abstract
Fake news has become a major challenge in today’s digital environment, particularly in languages where labeled data is limited. Most existing research has primarily focused on English due to the easy availability of annotated datasets, whereas low-resource languages such as Bengali remain underexplored. This study presents a multilingual approach for fake news detection using machine learning with contextual-based feature extraction. The proposed method integrates n-gram techniques with sentence-level contextual embeddings to capture both word-level patterns and semantic meaning. Since labeled data is not available for the Bengali dataset, a translation-based strategy is employed, followed by a pseudo-labeling process to assign labels automatically. The models are trained on English news titles and subsequently evaluated on both English and Bengali datasets to examine their cross-lingual effectiveness. The experimental findings indicate that ensemble-based classifiers such as Random Forest and Gradient Boosting achieve reliable performance across both languages. In some cases, the results for Bengali data are comparable or slightly better than those for English. The study demonstrates that effective fake news detection is possible in low-resource languages using short text data without relying on manually labeled datasets. The proposed approach provides a simple and efficient solution for multilingual fake news detection in data-scarce environments.
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.
Umme Noor Us Saqa, Sreenivasa B. R.· International Journal of Inn...· 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
The production of misinformation on digital platforms has been a concern in recent years, especially in interlingual contexts – people who create content in different languages and language elements that are integrated into written content.the existing fake news detectors use mostly only one language, and they are not adequately cross-linguistically generalizable in using the valuable detectors especially with low-resource languages. To overcome these weaknesses, the present paper suggests an LLM-based multilingual fake news detector which combines cross-lingual semantic alignment and contextual reasoning. The proposed approach involves the fine-tuned multilingual language model, which involves a transformer to derive contextual embeddings of multilingual text. The semantic alignment process (contrastive) is used to project the representation of the various languages to a common embedding space, and hence the transfer of knowledge. Moreover, semantic inconsistencies and misguiding tendency contribute to the reasoning of the model deducing abilities applied in the presence of attention processes. Experiments with the mixed English, Hindi, and Tamil data reveal that the proposed model leads to the accuracy of 94.3 and the F1-score of 93.6, which is better than a baseline model, including SVM, LSTM, and mBERT. The results enable mentioning the great overallization possibility and low-resource, multilingual applicability of the model. The paper gives a scaled and efficient approach to tackling the real-life aspect of multilingual fake news detection.
T. Divya, A. Meenakshi· International Conference Com...· 0 citations
The spread of misinformation through digital plat-forms such as social media sites and news portals has posed a problem of maintaining information credibility and building trust. Manual approaches alone cannot help cope with the huge volumes of information uploaded on these platforms every day. An automatic approach for fake news detection which utilizes NLP, sentiment analysis, semantic embedding methods and several machine learning algorithms has been developed in this paper. The news headlines collected from FakeNewsNet dataset have been pre-processed via tokenization, stop-word elimination, lemmatization, and n-grams extraction. Doc2Vec approach has been employed to extract semantic vectors whereas sentiment analysis has been done with the help of VADER tool. These semantic vectors have been provided as input to various machine learning algorithms such as Logistic Regression, Linear SVM, Random Forest, Gradient Boosting, XGBoost, LightGBM, Naïve Bayes and K-Nearest Neighbor. Experimental results suggest that ensemble learning models outperform other forms of machine learning techniques. Out of all the tested algorithms, ExtraTrees performed with the highest classification accuracy (77.54%) whereas XGBoost produced the highest macro F1-Score (0.5059).
Sneha Patle, Bhushan Gedam, Sameer Tembhurney et al.· International journal of com...· 0 citations
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
Fake news is spreading quickly on the internet, which is very bad for society and the security of the government. The significant issue that was talked about in the paper was the creation of automatic systems that can detect fake news better and adapt to various areas. The dataset used in the study is the LIAR dataset, which is a standard set of various political statements labeled with varying degrees of truthfulness. Text is also cleaned up, tokenized, and represented with existing trained word embeddings such as GloVe and Word2Vec as a step in data preparation. To identify complex trends in the text, most language and contextual features are removed, such as syntactic, semantic, and sentiment-based ones. The primary contribution of this study is a way of grouping various features into one representation. A set of models is subjected to performance tests, and it includes Random Forest, Naive Bayes, Convolutional Neural Network (CNN), Autoencoder, and a proposed Hybrid CNN-Autoencoder architecture. The hybrid model performs the most, having the greatest precision and the most equalized classification scores. Comparative analysis demonstrates that the combination of deep learning and knowledge of the environment significantly enhances the level of detection in domains. It is a flexible AI-based system that can work in the context of language and political differences and is a big step forward in searching for fake information automatically.
Pundlik Dattatray Jadhav, R. K. Shukla· international journal of eng...· 0 citations
