Approaches to Detect and Manage the Spread of Fake News on Social Media: Desktop Survey

Abstract

Fake news is information that has been created for personal benefit. In the past twenty years, the internet has created additional avenues for the widespread dissemination of inaccurate information. Fake news lacks the editing standards and methods that news companies use to confirm the authenticity and validity of material. As social media has evolved into a key news source for its users, fake news can be destructive and have a wide-ranging impact. Since it might be difficult to distinguish between an honest attempt at deception and an untruthful presentation of a questionable perspective, it can be difficult to recognize false information. The research articles used in this paper were obtained from google scholar and other online materials with the utilization of keywords. The articles were searched selectively based on their date, specifically excluding papers from 2015 and older. The analysis of the articles indicated various fake news detection approaches such as knowledge-based, linguistic, AI-based, machine learning, and hybrid techniques. All approaches showed an overall accuracy of above 70%. It is recommended that media literacy be integrated into mainstream educational courses and constantly improved to meet current demands and reach people with no formal education. Media literacy education can help to manage the non-technical issues of the methodologies used to detect fake news. For future developments, this paper proposes that every electronic device have a default program imbedded in it that detects fake news, or that every social media platform include a fake news detector in their security policy that monitors and detects any fake news posted.