Leveraging Machine Learning and Artificial Intelligence for Accurate Detection and Automated Flagging of Misinformation and Fake News

Fake news is an issue that is becoming more and more widespread in today’s culture. It may influence real world events via popular opinion and political consequences. In recent years, machine learning methods have gained prominence for false news detection and identification. In this study, we provide a comprehensive assessment of the literature on fake news detection based on machine learning, including single model and multi model, supervised and unsupervised techniques. We test these strategies on different data sets and provide some thoughts on benefits and downsides. One of the important innovations of our system is the provision of prediction confidence ratings that provide users an insight of the amount of confidence of the model in identifying news articles as real or fraudulent. This openness improves users’ ability to critically analyse the model’s predictions and make informed decisions. The system efficacy and the model calibration and resilience are evaluated in detail on enormous and diverse datasets on a wide variety of subjects and sources. We explore some of these challenges and unresolved issues, such as the need for more diversified data sets, the need of interpretability, and the risk of adversarial assaults. The testing reveals the ability of multi-modal classification model to identify false news with 98.3% accuracy and outperform the models, viz., Random Forest, Logistic Regression, SVM.