Application of Machine Learning Algorithms for Fake News Detection: A Review

Mohammed Ajuji, Rahinatu Mohammed Hamid, Datti Useni Emmanuel, Abdulqadir Maigari Turaki, Amina Nuhu Muhammad

Abstract


Before the advent of the internet and information and communication technology. News and/or information are broadcast via television, newspapers, and magazines are also known as traditional media. The recent advances of communication technologies have considerably transcended the chronological and spatial boundaries of traditional ways of disseminating, sharing, and spreading news especially in terms of speed and ease of access. These technologies which bring people together irrespective of geographical location have created a revolution in information generation and dissemination; online people interaction, human behaviour-related data, and even real-time sharing of data. However, the abuse of social technologies such as social media platforms nowadays has become a source of concern to many stakeholders. It has given some people ample opportunity to share false, fabricated, deceitful, and hoax information also known as fake news online. In this paper, we systematically reviewed articles that applied machine learning techniques to detect fake news on social media. Various machine learning techniques were applied with the most being Naïve Bayes. Decision Tree, Support Vector Machine, and Random Forest are commonly applied. Some techniques are least utilized whereas others like Ridge Regression, Lasso Linear Regression, and Elastic Net Regression have not been explored.

 



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