Conversational Artificial Intelligence

5Extracting and Analyzing Factors to Identify the Malicious Conversational AI Bots on Twitter

Gitika Vyas^1*, Piyush Vyas¹, Prathamesh Muzumdar², Anitha Chennamaneni¹, Anand Rajavat³ and Romil Rawat⁴

¹Computer Information Systems, Texas A&M University-Central Texas, Killeen, Texas, USA

²University of South Florida, Florida, USA

³Department of Computer Science Engineering, Director, Shri Vaishnav Institute of Information Technology, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, India

⁴Department of Computer Science, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, India

Abstract

On social media, many third-party vendors utilize Conversational Artificial Intelligence (CAI) to use social bots to spread marketing campaigns and to increase followers, thereby opening the door for malicious bots to compromise the security on social media, especially on Twitter where the posts are concise, making it hard to distinguish between human written posts and bot-generated tweets. Thus, such malicious bots on Twitter can break into user accounts, spread misinformation, breach account data, and market advertising spam. Hence, this chapter aims to conduct a detailed exploratory study to identify the crucial Twitter account features that help to detect malicious bots. Machine learning-based techniques such as information gain, correlation, and chi-square feature selections are used in this chapter to select the top feature set by comparing all three techniques. Twitter account data provided ...