Accounting to the expeditious digitization across all channels and mediums, the menace of fake news has been burgeoning at a colossal scale. Majority of the countries all across the world are trying to combat this challenge. This paper explores the application of Natural Language Processing and Machine Learning techniques to identify fake news accurately. Pre-processing tools are used to clean the data and apply feature extraction on them. Then a fake news detection model is built using four different techniques. Finally, the paper investigates and compares the accuracy of techniques which are Naive Bayes, Support Vector Machine (SVM), neural network and long short-term memory (LSTM) to find the best fit for the model.
Volume 11 | 06-Special Issue
Pages: 942-947