Jul 2026· International Scientific Journal of Engineering and Management· 0 citations
TL;DR
This research paper presents a comprehensive study of an AI-based fake news detection system leveraging Natural Language Processing techniques and multiple machine learning algorithms to automatically classify news articles as real or fake.
Abstract
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
The rapid advancement of large language models has introduced AI-generated fake news alongside traditional human-written misinformation, raising questions about whether detectability depends on the source of deceptive content. This study examines that issue through two controlled binary classification tasks: distinguishing real news from human-written fake news and from AI-generated fake news. Each article is represented using features related to lexical diversity, readability, and emotional characteristics, and evaluated with several machine learning models, including logistic regression, random forests, support vector machines, gradient boosting, neural networks, and ensemble methods. Performance is measured using the area under the receiver operating characteristic curve (AUC). Across all models, AI-generated fake news is detected with near-perfect accuracy, while human-written fake news is substantially more difficult to distinguish from real news. Because both tasks use the same modeling pipeline, this performance gap reflects intrinsic statistical differences in the text rather than methodological variation. Feature-level analysis shows that AI-generated fake news exhibits more uniform readability and emotional patterns, producing less overlap with real news. These findings reveal a key asymmetry in misinformation detection: current methods may be highly effective at identifying AI-generated content but remain less reliable against sophisticated human-authored misinformation. Detection systems should therefore account for the source of misinformation and continue adapting as generative models evolve.
Canliv Ibenye, Aya-Vera Jimenez, Samuel Jaeger et al.· 0 citations
The current landscape of digital and social media has made any news universally available. Yet, today's media is marred by dissemination of fabricated news. Known as fake news, it erodes the trust of society, leads to social disorder, and hinders social order. This paper presents a machine learning methodology to detect fake news. Natural language processing (NLP) is a branch of computer science that aids in detection of fictitious news. In this case, NLP is used to pre-process news articles. This includes, but is not limited to, text completing, tokenization, the elimination of stop words, and feature extraction by means of term frequency and inverse document frequency (TF-IDF). News articles are then classified as real or fake by various machine learning classification algorithms. These include, but are not limited to, logistic regression, Naïve Bayes, random forest, and support vector machines. A variety of metrics are used to determine the efficacy of each algorithm. These include accuracy, precision, recall, and the F1-score. The machine learning and NLP methods presented in this paper prove that they have the capability to identify misleading news, and this work provides a starting point for machine learning and NLP methods in fictitious news detection. It also describes the hurdles in reliable fictitious news detection and identifies areas for future research in improving reliable and effective fictitious news detection frameworks.
R. B, Sree Dhanya C, Udayakumar C· International Journal of Sci...· 0 citations
The rapid growth of online news platforms and social media has made it easier for information to spread quickly, but it has also increased the circulation of content that may create fear, misinformation, or potential security concerns. Detecting threat-related news at an early stage is essential for supporting public safety and informed decision-making. This study presents a hybrid lexicon-driven news threat detection framework that combines the NRC Emotion Lexicon with advanced machine learning models, namely Random Forest and XGBoost. Initially, news articles undergo preprocessing steps such as text cleaning, tokenization, and stop-word removal to improve data quality. Emotional features are then extracted using the NRC Lexicon and integrated with textual features to create an informative dataset for classification. The processed data is used to train and evaluate both machine learning models using performance measures including accuracy, precision, recall, and F1-score. Experimental results indicate that the proposed hybrid approach effectively identifies threatrelated news, with the Random Forest model providing slightly better classification performance than XGBoost. The combination of emotion-based lexical analysis and ensemble learning enhances prediction accuracy, making the proposed framework a practical and reliable solution for intelligent news threat detection in real-world applications.
G Bhavani Prasad, G.Rajini· International Journal of Eng...· 0 citations
An Explainable Artificial Intelligence (XAI) framework for fake news detection that unites the complementary explainability methods: SHapley Additive exPlanations and Local Interpretable Model-agnostic Explanations with the feature extraction technique, Term Frequency-Inverse Document Frequency (TF-IDF) and the Linear Support Vector Machine (Linear SVM) classifier.
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
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