AI and Fake News: A Conceptual Framework for Fake News Detection

Abstract

In today’s world, Cyberspace plays an essential part in an individual’s life. Many people heavily depend on social media to get information and read the news. Such excessive reliance on Cyberspace, specifically on social media, has created vast room for many cybercrimes, such as the rapid spread of Fake News and misinformation. Additionally, the possibility of generating fake compelling content has become more accessible. Thanks to the rapid growth of the Internet and the adaption of Artificial Intelligence (AI) technologies. AI technologies are a two-edged sword. They are capable of positive improvements, e.g. detecting misinformation, fake or altered images and videos, identifying bots, and processing and retaining information better than humans. On the other hand, when used by malicious actors, there is a significant threat to the digital, physical, and political landscape. Additionally, the increasing use of social media platforms, specifically Facebook and Twitter, has allowed the public to spread opinions and information quickly, whether factual or not. Therefore, there is a need for further research and collaboration to understand how to identify and combat the spread of fake news and disinformation and prevent the malicious use of AI technologies whilst preventing infringement of privacy guidelines. To this end, in this study, we propose a conceptual framework to classify and detect fake news. The three-tier framework features characterisation and feature extraction, classification and detection, and the final feature is defence.