Detecting fake news is a critical challenge in natural language processing (NLP), demanding solutions that balance accuracy, interpretability, and computational efficiency. Despite advances in NLP, systematic empirical benchmarks that directly compare both classical and deep models—across varying input richness and with careful attention to interpretability and computational tradeoffs—remain underexplored. 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—GRUs). 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 empirical tradeoffs between representational capacity, generalization, and interpretability. Our results show that transformer-based models, especially ALBERT, achieve state-of-the-art performance (macro F1 up to 0.99) with rich context, while classical ensembles remain viable for constrained settings. These findings directly inform practical fake news detection.

Social media platforms have emerged as valuable sources for mental health research, enabling the detection of conditions such as depression through analyses of user-generated posts. This manuscript offers practical, step-by-step guidance for applying machine learning and deep learning methods to mental health detection on social media. Key topics include strategies for handling heterogeneous and imbalanced datasets, advanced text preprocessing, robust model evaluation, and the use of appropriate metrics beyond accuracy. Real-world examples illustrate each stage of the process, and an emphasis is placed on transparency, reproducibility, and ethical best practices. While the present work focuses on text-based analysis, we discuss the limitations of this approach—including label inconsistency and a lack of clinical validation—and highlight the need for future research to integrate multimodal signals and gold-standard psychometric assessments. By sharing these frameworks and lessons, this manuscript aims to support the development of more reliable, generalizable, and ethically responsible models for mental health detection and early intervention.

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.

This paper conducts an in-depth study on the theoretical foundations and optimization mechanisms of instruction tuning for multi-task generalization. We propose a unified, parameter-efficient tuning framework that integrates instruction embedding modeling, task similarity regularization, and gradient alignment. The goal is to enhance the generalization and robustness of large language models under complex task combinations. Current instruction tuning methods often suffer from representation shift, objective conflict, and gradient interference when handling heterogeneous tasks. To address these issues, we propose a systematic solution from both structural design and optimization perspectives. Methodologically, we introduce semantically aligned instruction encodings to improve representation consistency across tasks. During optimization, we apply gradient projection to reduce inter-task update conflicts and adopt a dynamic weighting strategy based on gradient variation to enhance training stability and coordination. On the theoretical side, we construct an upper bound on generalization error based on Rademacher complexity and KL divergence of distributional shifts. This provides a formal characterization of the performance boundaries in multi-task instruction tuning. We conduct multiple experiments on the Super-NaturalInstructions dataset. The evaluation covers various aspects, including different instruction formulations, generalization to unseen tasks, and robustness under task combinations. Results show that the proposed method outperforms baselines on key metrics. The findings confirm its effectiveness in improving generalization under high task heterogeneity and reducing the risk of conflicts during cross-task learning.

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.