A Systematic Analysis of Machine Learning and Deep Learning Techniques to Detect Fake News

Expanding social media platforms produce a large amount of news daily that is not always accurate, true, or reliable. Some dishonest news vendors intentionally generate misinformation to misguide public views, manipulate people’s opinions, and further create a political agenda or confusion for their economic benefit. Automated techniques such as Machine Learning (ML) and Deep Learning (DL), are frequently used to prevent the spread of fake news. This work conducts a systematic assessment of the existing ML and DL strategies with a focus on data pre-processing, feature extraction, feature selection, and contextual analysis that enhance the performance of the fake news detection model. A comparison-based description of the fundamental models of ML and DL is provided, where it is found that DL techniques are more explainable and perform well in detecting fake news than ML classifiers.