Paper Contents
Abstract
In the era of information proliferation, the challenge of identifying and mitigating the impact of fake news has become increasingly critical. This research delves into the realm of fake news detection, aiming to enhance the efficacy of current methodologies through the utilization of a stacked ensemble approach. The study provides a comprehensive examination of existing techniques and identifies gaps in their performance. Leveraging this analysis, a novel machine learning model is proposed, designed to capitalize on the strengths of diverse algorithms within a stacked ensemble framework. The proposed model undergoes rigorous evaluation using diverse datasets, encompassing various types of fake news scenarios. The study explores the synergistic effects of combining multiple classifiers, each contributing unique insights into the complex landscape of misinformation. Results demonstrate significant improvements in accuracy, precision, and recall, establishing the superiority of the stacked ensemble approach over individual models. Additionally, the research investigates the interpretability of the ensemble, shedding light on how each component contributes to the overall decision-making process. The study also addresses potential challenges and provides insights into the robustness and generalizability of the proposed model. This comprehensive exploration aims to advance the field of fake news detection, offering a valuable contribution to the ongoing efforts to safeguard the integrity of information in an interconnected and information-driven society. The findings presented herein not only contribute to the theoretical understanding of stacked ensembles in the context of misinformation but also offer practical insights for the development of more effective and reliable fake news detection systems.
Copyright
Copyright © 2025 MICKEY SAHU. This is an open access article distributed under the Creative Commons Attribution License.
