Affiliation:
1. SASTRA University, India
2. Sathyabama Institute of Science and Technology, India
3. Asia Pacific University of Technology and Innovation, Malaysia
Abstract
Newspapers were the primary source of receiving news. Though they were slow in getting us the news, they were reliable since almost every piece of an article printed in newspapers is proofread. But things are changing rapidly and we are reliant on other sources for news (such as Facebook, Twitter, YouTube, WhatsApp). This paved the way for information, whether it is fake or real, that has never been witnessed in human history before. However, ever since social media boomed and the spread of information became easy, it has been difficult to find and stop the spread of fake and fabricated news. Existing solutions identify fake news usage either or some of the machine learning algorithms. In this work, an ensemble machine learning model is developed using ensemble method and evaluate their performance for the computation time to increase the accuracy of fake news detection using datasets. The experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners.