Identifying Fake News On ISOT Data Using Stemming Method With A Subdomain Of AI Algorithms

Abstract

This study experiments with the AI classification models coincident with the stemming techniques, in order to set up the fake news detection in the dataset provided by the International Social Ontology of Texts. With the SVM, Naive Bayes, RNN, and CNN models, the research will perform the evaluation of their capacity to autonomously distinguish genuine and fake news articles. It is seen that RNNs are an excellent tool for modeling short and long-range dependencies, while CNNs are more skilled in identifying fakes dedicated to local patterns. One of the assimilative methods with AI algorithm tools, makes for high detection of truths, demonstrating the importance of team working within interdisciplinary areas in combating misinformation.