Interaction-Based Behavioral Analysis of Twitter Social Network Accounts

Abstract

This article considers methodological approaches to determine and prevent social media manipulation specific to Twitter. Behavioral analyses of Twitter users were performed by using their profile structures and interaction types, and Twitter users were classified according to their effect size values by determining their asset values. User profiles were classified into three different categories, namely popular-active, observer-passive, and spam-bot-malicious by using k-nearest neighbor (K-NN), support vector machine (SVM), and artificial neural network (ANN) algorithms. For classification, the study used the basic characteristics of users, such as density, centralization, and diameter, as well as suggested time series such as the simple moving average and cumulative moving average. The highest accuracy was obtained by the K-NN algorithm. The results obtained with K-NN for all classes were higher than the F1-Score values obtained for the other algorithms. According to the results obtained, classification accuracy values were found to reach a maximum of 96.81% and a minimum of 92.33%. Our classification results showed that the proposed method was satisfactory for popular-active, observer-passive, and spam-bot-malicious account separation.