Optimizing Misinformation Control: A Cloud-Enhanced Machine Learning Approach

Abstract

The digital age has witnessed the rampant spread of misinformation, significantly impacting the medical and financial sectors. This phenomenon, fueled by various sources, contributes to public distress and information warfare, necessitating robust countermeasures. In response, a novel model has been developed, integrating cloud computing with advanced machine learning techniques. This model prioritizes the identification and mitigation of false information through optimized classification strategies. Utilizing diverse datasets for predictive analysis, the model employs state-of-the-art algorithms, including K-Nearest Neighbors (KNN) and Random Forest (RF), to enhance accuracy and efficiency. A distinctive feature of this approach is the implementation of cloud-empowered transfer learning, providing a scalable and optimized solution to address the challenges posed by the vast, yet often unreliable, information available online. By harnessing the potential of cloud computing and machine learning, this model offers a strategic approach to combating the prevalent issue of misinformation in the digital world.