Interaction-Based Behavioral Analysis of Twitter Social Network Accounts

İş İş,Tuncer Tuncer

doi:10.3390/app9204448

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.

Highlights

In today’s world, where internet access is widespread, hardware can be integrated into micro-level devices and software is inclusive at the macro level, considerable changes have occurred in individual and social communication environments
Behavioral analyses of social media users were performed, and they were classified according to the characteristics and interactions of their accounts
The separation of popular-active, observer-passive, and spam-bot-malicious groups has an average accuracy of 96.81% with the help of the support vector machine (SVM), k-nearest neighbor (K-NN), and artificial neural network (ANN) algorithms

Summary

Introduction

In today’s world, where internet access is widespread, hardware can be integrated into micro-level devices and software is inclusive at the macro level, considerable changes have occurred in individual and social communication environments. As a result of increased opportunities and tools for internet access—due to the development of information technologies—the number of social media users has started increasing exponentially, thereby changing communication platforms, socialization areas, and topics [1,2]. Social networks can be used in numerous areas, such as friends, environment, knowledge acquisition, news, events, as well as announcement follow-up, marketing, and advertising. Social networks offer people various opportunities to share their ideas and thoughts instantaneously, to make news and announcements, and to make the advertising and marketing of their products, services, and brands versatile, without requiring any intermediaries.

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Discussion

Conclusion