Spam detection on Twitter using a support vector machine and users’ features by identifying their interactions

Abstract

Spam tweets might cause numerous problems for users. An automatic method is introduced as a proposed method to detect spam tweets. This method is based on pre-processing and feature extraction steps. The pre-processing step is significant for our problem due to the specific structure of tweets. The pre-processing step is performed in such a way that after which only the words remain in each tweet that can play a key role in determining whether the tweet is spam or non-spam. In the proposed method, the features are classified into five classes of user profile features, account information features, user activity based features, user interaction based features, and tweet content-based features including 28 different features. In the feature selection step, an optimal subset of these features is selected for the learning process. However, a support vector classifier is used for the learning process by two Gaussian and polynomial kernels. Finally, the proposed method is compared with multi-layer perceptron (MLP), Naive Bayes (NB), random forest (RF), and k-nearest neighbors (KNN) methods in terms of standard criteria. The obtained results show the superiority of the proposed method using support vector machine (SVM) algorithm and polynomial kernel with 0.988 precision, 0.953 efficiency, 0.96 accuracy, F-0.969, and 0.985 ROC area under the curve compared to the other methods, indicating that the proposed method has better performance overall.