Fake News Detection On Social Media:Review of Literature

Abstract

In today's world, social media has become one of the most accessible sources of news for people worldwide due to its low cost, easy accessibility, and rapid dissemination. However, the harmful effects of fake news on individuals and society have been evident for a long time. Despite the challenges in detecting false information, numerous scholars are working to understand the issue and its characteristics.

 The purpose of this paper is to analyze contemporary frameworks that make use of different machine learning techniques in order to further our understanding of false news identification. The efficiency of these frameworks in recognizing and halting the spread of false information may be compared by examining various methods. A particular technique was used in the review process to provide a thorough and instructive assessment.

Even though a lot of research has been done to identify false news, more can be done in the future. These studies have many limitations such as biased data, adaptability, credibility analysis and the news is only categorized as legitimate or unauthentic by their solutions. Nevertheless, a system of ratings or scores to assess the reliability of news is necessary for a workable approach.