Are We Secure from Bots? Investigating Vulnerabilities of Botometer

Social media platforms such as Twitter provide an incredibly efficient way to communicate with people. While these platforms have many benefits, they can also be used for deceiving people, spreading misinformation, manipulation, and harassment. Social bots are usually employed for these kind of activities to artificially increase the amount of a particular post. To mitigate the effects of social bots, many bot detection systems are developed. However, the evaluation of these methods are challenging due to lack limited available datasets and the variety of bots people might develop. In this work, we investigate vulnerabilities of state-of-the-art Botometer social bot detection system by creating our own bot scenarios instead of using public datasets. In our experiments, we show that Botometer is not able to detect our social bots, showing that we need more enhanced bot detection models and test collections to better evaluate systems' performances.

I. Introduction

Online Social Networks (OSNs) such as Twitter and Facebook become an important part of our daily life. They are highly efficien and useful platforms for communicating with others, advertising, and retrieving information we need at various topics. Actually we become more dependent on these platforms as the use of social media grows linearly by years [6].