Using Machine Learning to Protect Social Media Accounts from Spam Emails

Abstract

Spam refers to all forms of bulk, unsolicited, unwelcome digital communication. Spam can be transmitted by text messages, phone calls, social media, or email, which is how it is frequently done. The rise of social media websites like Facebook, Twitter, YouTube, etc. has given spammers increased access to people. Most spam filtering solutions employ text-based algorithms. The fundamental difficulty is classification because it is a significant one. In this study, features are extracted from emails using classifications of rules. To automatically categorize emails, numerous systems have been created, a few of them include systemic decision-making. The use of Bayesian classifiers, networked neurons, support vector machines, and sample-based approaches. A sizable and time-consuming labelled dataset is required for the supervised model training phase. SVM and Naive Bayes, two supervised learning algorithms, outperform other models in terms of spam identification. It provides comprehensive explanations of these methods in addition to several likely areas for future research in spam filtering and detection. Because most spammers employ obfuscation techniques, the rules set need ongoing updating and improvement. Refinement is occasionally necessary, and in most situations, it is automated, so end users have less bother.