A Comprehensive Review of Detection Models for Automation in Avoiding Fake News

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Archana Nanade, Alok Kumar, Ashutosh Gupta

Abstract

In the digital era, the explosion of fake news has become a critical challenge, posing threats to public opinion, political processes, and societal harmony. Manual fact-checking is insufficient to tackle the rapid spread of misinformation, necessitating the use of automated tools. This paper explores the need for automation in combating fake news and provides an extensive review of various models available for fake news detection, focusing on machine learning, deep learning, and hybrid approaches. The study includes an in-depth analysis of the TweetTruth framework, comparing it with existing models and highlighting the advantages of automation in addressing this pressing issue..

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