Open Access

ARTICLE

Fake News Classification: Past, Current, and Future

by Muhammad Usman Ghani Khan¹, Abid Mehmood², Mourad Elhadef², Shehzad Ashraf Chaudhry^2,3,*

1 Department of Computer Science, University of Engineering and Technology, Lahore, 54890, Pakistan
2 Department of Computer Science & Information Technology, Abu Dhabi University, Abu Dhabi, 59911, United Arab Emirates
3 Department of Software Engineering, Faculty of Engineering and Architecture, Nisantasi University, Istanbul, Turkey

* Corresponding Author: Shehzad Ashraf Chaudhry. Email:

Computers, Materials & Continua 2023, 77(2), 2225-2249. https://doi.org/10.32604/cmc.2023.038303

Received 07 December 2022; Accepted 17 April 2023; Issue published 29 November 2023

Abstract

The proliferation of deluding data such as fake news and phony audits on news web journals, online publications, and internet business apps has been aided by the availability of the web, cell phones, and social media. Individuals can quickly fabricate comments and news on social media. The most difficult challenge is determining which news is real or fake. Accordingly, tracking down programmed techniques to recognize fake news online is imperative. With an emphasis on false news, this study presents the evolution of artificial intelligence techniques for detecting spurious social media content. This study shows past, current, and possible methods that can be used in the future for fake news classification. Two different publicly available datasets containing political news are utilized for performing experiments. Sixteen supervised learning algorithms are used, and their results show that conventional Machine Learning (ML) algorithms that were used in the past perform better on shorter text classification. In contrast, the currently used Recurrent Neural Network (RNN) and transformer-based algorithms perform better on longer text. Additionally, a brief comparison of all these techniques is provided, and it concluded that transformers have the potential to revolutionize Natural Language Processing (NLP) methods in the near future.

---

Keywords

---