Social networking services were designed to bring individuals from all over the world together and provide them a place to express their views and opinions. However, since their inception, social media platforms such as Facebook, Instagram, and Twitter have been misused for harmful purposes including publishing inaccurate information and promoting fake news. Surprisingly, due to its accessibility and wide range of topics, more individuals are turning to social media to consume news, rather than conventional news sources like newspapers and television. Recently, classification of fake news has caught the attention of many researchers, and there is an increasing demand for controlling the spread of fake news among these networking sites. In this manuscript, we have presented a method for classifying false information using TF-IDF vectorizer and Natural Language Processing. For training and evaluating the performance of the model we have used a dataset from Kaggle and Buzzfeed News. Our model shows promising results.

Keywords : Fake News, Machine Learning, Term Frequency, Inverse Document Frequency, Vectorizer, Natural Language Processing.)