Jun 2026· Journal of Military Technology· Vol 9, pp. 61-68· 0 citations· 15 references
TL;DR
This paper explores the application of natural language processing and machine learning techniques to automatically detect and classify fake news articles and develops a web-based application to integrate the trained models into an interactive platform.
Abstract
1 Abstract —The proliferation of fake news has become a significant challenge in the digital era, with misinformation spreading rapidly across online platforms and social media. This paper explores the application of natural language processing and machine learning techniques to automatically detect and classify fake news articles. Multiple classification models, including traditional algorithms and modern deep learning architectures, were trained and evaluated on a dedicated dataset. To analyze the robustness of these models, adversarial attacks were applied using the TextAttack framework. Such attacks simulate subtle modifications in the input text, exposing potential vulnerabilities that may lead to misclassifications. In addition, explainability techniques such as LIME were employed to interpret model predictions and to better understand the factors influencing decision-making. A web-based application was developed to integrate the trained models into an interactive platform. The system allows users to analyze news articles by either providing the full text or submitting a URL, while administrators have access to model explanations, database management, and retraining functionalities.
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
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
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
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
Modern machine learning methods such as Generative Adversarial Networks (GANs) are used to automatically produce the fake video content that looks completely real. This threatens the integrity of digital media, journalism, and the public trust. In response to these threats, the DeepFake Reporter Guard offers an explainable AI-based web platform for the journalists to verify the authenticity of a news video. For regional specificity, this system uses XceptionNet model, which was trained on the INDIFACE dataset consisting of Indian faces. The detection methodology involves measuring the facial inconsistencies across the frame level. Once deepfake videos are identified, Grad-CAM-based explainability highlights the manipulated frames in the video and provides visual explanation. The explanation is presented in a way that non-technical users can understand it. The user-friendly web interface allows the journalists or users to upload the videos and get the instant authenticity reports telling whether the video is real or fake with a clear justification. The proposed method is robust and adaptable, making it suitable for real world situations.
Shaik Afreen Hasen, M. B, Namburi Sriya Ram· 2026 7th International Confe...· 0 citations
HEF-XFND is proposed, a hybrid explainable feature-fusion framework that combines sparse lexical evidence, contextual transformer representations, source-level credibility indicators, and calibrated ensemble learning that addresses three recurring limitations in fake-news research.
Raju M, Subalakshmi Kannan, P. P· International journal of res...· 0 citations