Analysis of Spam Detection Algorithms in Machine Learning-Based Facebook Social Media Platform

Abstract

analysis of spam detection algorithms in Facebook's social media platform using a machine learning approach. With the increasing number of spam disrupting the user experience, effective detection is essential. The machine learning approach allows the system to learn patterns of spam behavior from historical data. The study compared various machine learning algorithms, such as Naive Bayes, Support Vector Machines, and Neural Networks, to determine which are most effective at detecting spam. The results of the experiment showed that the Neural Networks algorithm achieved the highest accuracy in identifying spam content on the Facebook platform. This research provides valuable insights for the development of more sophisticated spam detection systems in the social media environment.