Analyzing the Application of Machine Learning for Detecting False Information in News Articles: A Comprehensive Review - Jassal, Abhay; Singh, Maninder (Journal of Advanced Research in Information Technology, Systems and Management; Vol 7 No 2 (2023): Journal of Advanced Research in Information Technology, Systems and Management, 2023)
In the digital age, the rapid dissemination of information through the internet and social media has brought about a pressing concern: the proliferation of fake news. Fake news, a form of disinformation, poses serious risks, including the spread of false information, manipulation of public opinion, and even incitement of violence. To combat this challenge, machine learning techniques have emerged as potent tools for detecting and mitigating the impact of fake news. This article explores the pivotal role of machine learning in the identification of fake news and its significance in upholding the integrity of information in the digital era. Fake news is not a new phenomenon, but it has gained prominence due to the ease of content creation, the influence of social media, and a growing lack of media literacy. The proposed approach of using a machine learning ensemble to classify news articles is a promising solution to this problem. By exploring different textual properties, the model can accurately distinguish between real and fake news articles. The use of ensemble methods, which combines multiple machine learning algorithms, further improves the performance of the model. The experimental evaluation confirms that this approach outperforms individual learners, demonstrating its potential to be a valuable tool in the fight against misinformation and disinformation. Overall, this study highlights the importance of using technology to combat the negative effects of social media and online news.