Machine learning technique for online fake news detection
DOI:
https://doi.org/10.15282/jgi.8.1.2025.11289Keywords:
Fake news, Support vector machine, Naïve Bayes, Decision tree, GovernanceAbstract
The Internet has become a fundamental part of our daily lives, influencing social, political, and other domains. In the modern era, social media has emerged as a powerful tool for individuals and organizations globally. While social media offers significant benefits, such as the ability to disseminate news globally, it also poses the risk of spreading false information. Fake news can be particularly harmful as it spreads rapidly and without limits, potentially triggering political disagreements, harming mental health, and impacting the economy. This research delves into the phenomenon of fake news in detail, proposing detecting fake news through machine learning techniques. Eminie Bozkus provided a dataset for the training and testing processes. This research aims to evaluate various algorithms' accuracy and compare their effectiveness in detecting fake news.
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