Tweet Spam Detection Using Machine Learning and Swarm Optimization Techniques

Abstract

Social media networking platforms connect people living in every corner of the world. Twitter has now become a popular microblogging service, allowing users to express themselves and keep up with current events. Twitter has attracted spammers because to its popularity and ease of use. As a result, spam identification (ID) has become one of the most pressing issues. It is vital to detect and filter spam tweets as well as their owners in order to provide a spam-free environment. In this article, a spam detection method is proposed using a swarm optimization approach on a tweet-by-tweet basis. A spam tweet detection dataset is used to train the machine learning (ML) model. Metaheuristic features are created based on the input features in the dataset. Whale swam optimization algorithm (WOA) is used to select the important features before classification. The conventional objective function of WOA is modified into stochastic gradient descent (SGD) to perform feature selection. The selected subset of features is used to train the Adaboost (AB) classifier to detect the spam in the tweets. The AB classifier produced the best results in combination with WOA and SGD. The obtained accuracy is 99.85% in testing with a minimum subset of seven features and in the least possible minimum time of 17.9 s.