Comparative Analysis of Fake News Identification Using Machine Learning Methods

The usage of social networks is growing exponentially day by day. Users can obtain a lot of information from social networks where some genuine information as well as some misinformation are there that mislead users and may face problematic situations. Therefore, it is mandatory to identify fake content. Thus, the main purpose of this study is to examine the state of the art in the detection of fake content and the contribution of machine learning algorithms such as Random Forest, Support Vector Machine, Passive Aggressive Classifier, and Logistic Regression with Term Frequency-Inverse Document Frequency vectorization technique applied for detecting fake content in English and Hindi language efficiently. Performed comparison analysis between algorithms on the FakeNewsNet dataset in English and another dataset in the Hindi language to indicate the effectiveness of classification outcome. The Support Vector Machine classifier generated the greatest results out of the four.