Detection of Fake News and Rumors in Social Media Using Machine Learning Techniques With Semantic Attributes

Summary

Spreading rumors and fake news over popular social media like Facebook, Twitter, WhatsApp, etc creates serious social problems. It is observed that the administration had to stop Internet service in a large area to control the spreading of such rumors or fake news. Bar in the internet service creates another serious problem for the citizens as we are heavily dependent on the internet nowadays. To detect fake news, this work suggests a machine learning method using the Naïve Bayes classifier. The system can be plugged in with any social media, as it predicts the probability of news to be fake or rumor the same can be stopped from spreading farther with the software control in the social media. This model was trained and tested in three datasets (two of the English language and one of Bengali language). We have successfully achieved a high accuracy, which is 81% for the English language and 93% for the Bengali language. This is an acceptable result comparing the same with the recent works. Using deep learning or a combination of classifiers, the result may be improved further.