A Systematic Literature Review on Machine Learning Algorithms for the Detection of Social Media Fake News in Africa
Abstract
Fake news has been around in history before social media emerged. Social media platforms enable the creation, processing, and sharing of various kinds of content and information on the Internet. While the mediums of information and content shared across social media platforms are hard for users to authenticate, if users are tracking fake information or fake content, it can harm individuals, society, or the world. Fake news is increasingly becoming a worrisome issue, especially in Africa, because it's difficult to identify and stop the distribution of fake news. Due to languages and diversity, it is difficult for humans to understand and subsequently identify fake news on social media platforms, so high-level technological strategies, such as machine learning (ML), would be able to tell if the content is false material. As such, this study sought to identify effective ML classifiers to detect fake news on social media platforms, and the systematic literature review followed the PRISMA standard. The study identified 14 effective ML classifiers to manage fake news on social media platforms, including Random Forest, Naive Bayes, and others. Four research questions guided the study focused on the effectiveness of the classifiers, their applicability for detecting different forms of false news, the features of the dataset size and features, and the metrics that were created to assess the metrics. A conceptual framework known as the Information Behavioral Driven Social Cognitive Model (IBDSCM) was proposed in a bid to affect the fake news detection on social media platforms. Overall, this study establishes a contribution to understanding the ML algorithms for detecting false news in Africa and allows for a conceptual base for future studies.
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