Fake News Detection: Traditional vs. Contemporary Machine Learning Approaches

Abstract

Fake news is a growing problem in modern society. With the rise of social media and ever- increasing internet accessibility, news spreads like wildfire to millions of users in a very short time. The spread of fake news can have disastrous consequences, from decreased trust in news outlets to overturned elections. Such concerns call for automated tools to detect fake news articles. This study proposes a predictive model that can check the authenticity of a news article. The model is constructed using two different techniques to construct our model: (1) linguistic features and (2) feature extraction. We employed some widely used traditional (e.g. K-nearest neighbour (KNN) and support vector machine (SVM)) as well as state-of-the-art (e.g. bidirectional encoder representations from transformers (BERT) and extreme machine learning (ELM)) machine learning algorithms using feature extraction methods and linguistic features. After generating the models, performance metrics (e.g. accuracy and precision) are used to compare their performance. The model generated via logistic regression using feature hashing vectorisation emerged as the best model, with 99% accuracy. To the best of our knowledge, no extant studies have compared the traditional and contemporary methods in this context and demonstrated the traditional ones to be better performers. The fake news detection model can help curb the spread of fake news by acting as a tool for news organisations to check the authenticity of a news article.