Detecting Fake News using Machine Learning and Deep Learning Algorithms

Social media interaction especially the news spreading around the network is a great source of information nowadays. From one's perspective, its negligible exertion, straightforward access, and quick dispersing of information that lead people to look out and eat up news from internet-based life. Twitter being a standout amongst the most well-known ongoing news sources additionally ends up a standout amongst the most dominant news radiating mediums. It is known to cause extensive harm by spreading bits of gossip previously. Online clients are normally vulnerable and will, in general, perceive all that they run over web-based networking media as reliable. Consequently, mechanizing counterfeit news recognition is elementary to keep up hearty online media and informal organization. This paper proposes a model for recognizing forged news messages from twitter posts, by figuring out how to anticipate precision appraisals, in view of computerizing forged news identification in Twitter datasets. Afterwards, we performed a comparison between five well-known Machine Learning algorithms, like Support Vector Machine, Naïve Bayes Method, Logistic Regression and Recurrent Neural Network models, separately to demonstrate the efficiency of the classification performance on the dataset. Our experimental result showed that SVM and Naïve Bayes classifier outperforms the other algorithms.