Detection of fake news from social media using support vector machine learning algorithms

Abstract

Never happened before in human history the spreading of fake news; now, the development of the Worldwide Web and the adoption of social media have given a pathway for people to spread misinformation to the world. Everyone is using the Internet, creating and sharing content on social media, but not all the information is valid, and no one is verifying the originality of the content. Identifying the content's essence is sometimes complicated for researchers and intelligent researchers. For example, during Covid-19, misinformation spread worldwide about the outbreak, and much false information spread faster than the virus. This misinformation will create a problem for the public and mislead people into taking the proper medicine. This work will help us to improve the prediction rate. Here we investigate the ability of machine learning classifiers and deep learning models: Naive Bayes, Logistic Regression, Support Vector Machine, Decision Tree, Random Forest and K-Nearest Neighbor. Deep learning models include Convolutional Neural Networks and Long Short-Term Memory (LSTM). The various types of machine learning and deep learning models will be trained and tested using the Covid-19 dataset (1,375,592 tweets).