An Empirical Comparison of Fake News Detection using different Machine Learning Algorithms

Relying on social networks to follow the news has its pros and cons. Social media websites indeed allow the spread of information among people quickly. However, such websites might be leveraged to circulate low-quality news full of misinformation, i.e., "fake news." The wide distribution of fake news has a considerable negative impact on individuals and society as a whole. Thus, detecting fake news published on the various social media websites has lately become an evolving research area that is drawing great attention. Detecting the widespread fake news over the numerous social media platforms presents new challenges that make the currently deployed algorithms ineffective or not applicable anymore. Basically, fake news is deliberately written on the first place to mislead readers to accept false information as being true, which makes it difficult to detect based on news content solely; consequently, auxiliary information, like user social engagements on social media websites, need to be taken into account to help make a better detection. Using such auxiliary information is challenging because users' social engagements with fake news produce noisy, unstructured, and incomplete Big-Data. Due to the fact that fake news detection on social media is fundamental, this research aims at examining four well-known machine learning algorithms, namely the random forest, the Naïve Bayes, the neural network, and the decision trees, distinctively to validate the efficiency of the classification performance on detecting fake news. We conducted an experiment on a widely used public dataset i.e. LIAR, and the results show that the Naïve Bayes classifier defeats the other algorithms remarkably on this dataset.