Fake News Classification: Past, Current, and Future

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: Supervised Learning Algorithms, Fake News Classification, Online Disinformation, Transformers, Recurrent Neural, Network (RNN)