Enhancing News Authenticity Prediction with Machine Learning Approaches - Chikte, Pranav; Bundele, Tushar; Shrirao, Aditya; Thakare, Yogesh; Raut, Gaurav; Mohod, M. M. (Journal of Data Engineering and Knowledge Discovery, 2026)
The issue of misinformation becoming prevalent in social media platforms has now taken a turn and has become a significant threat to public discourse, democratic processes, and societal trust. Manual verification systems simply cannot keep pace with the sheer volume and velocity of fake news being generated, thus necessitating the activation of automated detection mechanisms. This time, a technique based on machine learning for the purpose of detecting false news with the passive-aggressive classifier plus term frequency-inverse document frequency (TF-IDF) vectorization is introduced. The model developed underwent training and evaluation on the basis of a benchmark dataset of 44,898 news articles, among which 23,481 were marked as fake while 21,417 were real. The system proposed was able to obtain 99.48% accuracy, reporting precision and recall scores of 0.99 for both classes, thus proving its effectiveness in differentiating the fabricated content from the real one. The algorithm is suitable for this task due to the fact that it requires minimal computational resources, is low-weight, thus allows the model to be deployed in real-time, and is also more cost-efficient than deep learning, which is resource-demanding. The strength of the proposed approach was established through the implementation of confusion matrix analysis and standard classification metrics. The purpose of this project is to connect the theoretical research with real-life use by creating an efficient and easily usable application for the counteraction of digitally spread misinformation.