Machine Learning Methods for Social Network Fake News Identification
Abstract
Today, identifying fake news is essential to stopping the spread of false information that could confuse and mislead people. It contributes to preserving public confidence in information sources and the media. Identifying false news guarantees that only accurate and trustworthy information reaches the audience in the age of the internet, where news spreads quickly through social media. This paper examines several machine learning methods for detecting false news, with a focus on supervised and unsupervised learning approaches. The study offers a comparative evaluation of different methods specially stressing their benefits and drawbacks. The study also identifies significant barriers such the lack of high-quality labeled data, changing misinformation tactics, and the problem of real-time detection. These drawbacks highlight the requirement for detection models that are more robust, flexible, and explicable. The purpose of this paper is to help researchers identify relevant methods for combating fake news and comprehend current trends.
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