Using Supervised Machine Learning Models and Natural Language Processing for Identification of Fake News

Abstract

Social media has gained popularity over the last decade due to its ease of access and providing large amount of information to people. In seconds, users are able to access information from social media related to politics, life-style, science and money other fields. However, data obtained from social media platforms represent a mixture of fake and real news. Fake news are in-tended to deceive people and change their attitudes and beliefs. Machine learning algorithms have shown successful in classifying real from fake news. Nonetheless when applying machine learning models in this context related to limitations in the dataset type, balance or skewness. Hence, data pre-processing is essential prior to application of machine learning models. Therefore, this work evaluated the use of supervised machine learning models with different data pre-processing approaches for classification of fake news obtained from social media platforms. Different pre-processing techniques have been applied related to feature extraction and feature selection alongside four machine learning models being logistic regression, decision trees, random forest and extreme gradient boost. The findings showed that random forest and extreme gradient boost with bi-gram feature extraction and chi-squared feature selection showed the best performance. Future work involves using the proposed model to detecting fake news in different con-text and different languages.