A Machine Learning-based Approach for Detecting Fake News in Online Media

Abstract

The proliferation of fake news in online media poses a significant challenge to information credibility, necessitating robust detection mechanisms. This study addresses the growing concern by proposing a machine learningbased approach, aiming to discern genuine news from fabricated stories effectively. We identify the deficiencies in existing systems, particularly their failure to adapt to the sophisticated and evolving nature of misinformation and their limited ability to process the subtleties of deceptive content. To bridge this gap, we explore the efficacy of three distinguished machine learning algorithms: Naive Bayes, Support Vector Machine (SVM), and Decision Trees, each with its unique strengths in text classification. Our methodology encompasses a comprehensive feature engineering process to capture the stylistic and semantic nuances of textual data, followed by rigorous model development and validation. We assess model performance through an array of metrics, including accuracy, precision, recall, and F1 score, to ensure a multifaceted evaluation of each algorithm's capabilities. The empirical analysis demonstrates that the SVM model achieves the highest accuracy, at 36.5%, marking it as the most proficient in our comparative study. The study's significant contribution lies in its detailed analytical approach, providing insights into the models' performance and laying the groundwork for future advancements in the field. Our findings not only enhance the current understanding of fake news detection but also pave the way for the development of more sophisticated and reliable detection systems. The overarching achievement of this research is the advancement toward a more trustworthy online information ecosystem, where the veracity of content can be ascertained with greater confidence.