Analyzing the Application of Machine Learning for Detecting False Information in News Articles: A Comprehensive Review

Abstract

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.