Evaluating Machine Learning Algorithms For Bengali Fake News Detection

In this world of modern technologies and media, online news publications and portals are increasing at a high speed. That is why, nowadays, it has become almost impossible to check out the traditional fact of news headlines and examine them due to the increase in the number of content writers, online media portals, and news portals. Mostly, fake headlines are filled with bogus or misleading content. They attract the commoners by putting phony words or misleading fraudulent content in the headlines to increase their views and share. But, the se fake and misleading headlines create havoc in the commoner's life and misguide them in many ways. That is why we took a step so that the commoners can differentiate between fake and real news. We proposed a model that can successfully detect whether the story is fake or accurate based on the news headlines. We created a novel data set of Bengali language and achieved our aim and reached the target using the Gaussian Naive Bayes algorithm. We have used other algorithms, but the Gaussian Naive Algorithm has performed well in our model. This algorithm used a text feature dependent on TF-IDF and an Extra Tree Classifier to choose the attribute. In our model, using Gaussian Naive Bayes we got 87% accuracy which is comparatively best than any other algorithm we used in this model.