Analysis of spammers’ behavior on a live streaming chat

Abstract

Live streaming is becoming a popular channel for advertising and marketing. An advertising company can use this feature to broadcast and reach a large number of customers. YouTube is one of the streaming media with an extreme growth rate and a large number of viewers. Thus, it has become a primary target of spammers and attackers. Understanding the behavior of users on live chat may reduce the moderator’s time in identifying and preventing spammers from disturbing other users. In this paper, we analyzed YouTube live streaming comments in order to understand spammers’ behavior. Seven user’s behavior features and message characteristic features were comprehensively analyzed. According to our findings, features that performed best in terms of run time and classification efficiency is the relevant score together with the time spent in live chat and the number of messages per user. The accuracy is as high as 66.22 percent. In addition, the most suitable technique for real-time classification is a decision tree.