Fake News Detection In Social Media Using Distributed Technology Of Machine Learning And Deep Learning

Abstract

Classification of fake news on social media has gained a lot of attention in the last  decade due to the ease  of adding  fake  content through  social media  sites. In  addition, people  prefer  to  get  news  on  social  media  instead  of  on traditional televisions. These trends have led to an increased interest in fake news and its identification by researchers. This study focused on classifying fake news on social media  with textual content (text classification). In this classification, four traditional methods were applied to extract features from texts (term frequency–inverse document frequency, count vector, character level vector, and N-Gram level vector), employing 10 different machine  learning  and  deep  learning  classifiers  to categorize the fake news dataset. The results obtained showed that fake  news with  textual content  can indeed  be classified, especially  using a  convolutional  neural network.  This study obtained an  accuracy  range  of 81  to  100%  using  different classifiers.