The proliferation of fake news presents a major challenge for information integrity in the digital age. In this work, we systematically compare the performance of five widely used machine learning and deep learning models—Logistic Regression, Random Forest, Light Gradient Boosting Machine (LightGBM), ALBERT, and Gated Recurrent Units (GRU)—on the WELFake dataset, utilizing only the news article headlines as input. Each model’s mathematical formulation and optimization process are described, and their ability to capture salient features in brief textual data is evaluated. Experimental results show that transformer-based ALBERT outperforms all other models in both precision and recall, demonstrating the value of contextual encoding even with limited input. Ensemble models, particularly Random Forest and LightGBM, also deliver strong performance and offer interpretability benefits for practical deployment. This study provides actionable insights for deploying efficient and accurate fake news detection models when only headline information is available.

Detecting fake news is a critical challenge in natural language processing (NLP), demanding solutions that balance accuracy, interpretability, and computational efficiency. In this study, we systematically evaluate the mathematical foundations and empirical performance of five representative models for automated fake news classification: three classical machine learning algorithms (Logistic Regression, Random Forest, and Light Gradient Boosting Machine) and two state-of-the-art deep learning architectures (A Lite Bidirectional Encoder Representations from Transformers—ALBERT, and Gated Recurrent Units—GRU). Leveraging the large-scale WELFake dataset, we conduct rigorous experiments under both headline-only and headline-plus-content input scenarios, providing a comprehensive assessment of each model’s capability to capture linguistic, contextual, and semantic cues. We analyze each model’s optimization framework, decision boundaries, and feature importance mechanisms, highlighting the mathematical tradeoffs between representational capacity, generalization, and interpretability. Our results reveal that transformer-based models, particularly ALBERT, achieve state-of-the-art performance, especially when rich textual context is available. Classical ensemble models remain competitive for resource-constrained and interpretable applications. This work advances the mathematical discourse on NLP by bridging theoretical model properties and practical deployment strategies for misinformation detection in high-dimensional, real-world text data.

Fraud detection in online transactions presents a challenging task due to the rarity of fraudulent events and the evolving nature of fraud strategies. This study presents a comparative analysis of three supervised machine learning models, Logistic Regression, Random Forest, and LightGBM, for detecting fraudulent transactions in an extremely imbalanced dataset. We evaluate each model under both standardized and raw feature preprocessing settings using macro-averaged metrics and AUC. Our findings show that ensemble-based models, particularly LightGBM, significantly outperform linear baselines and exhibit robustness to feature scaling. Additionally, we assess K-Means clustering as an unsupervised baseline, but observe that it fails to meaningfully separate fraud cases, suggesting the need for more informative features or hybrid learning approaches. These results offer practical insights into model selection, preprocessing, and the trade-offs between precision and recall in real-world fraud detection systems.