An empirical study of socialbot infiltration strategies in the Twitter social network
Online social networks (OSNs) such as Twitter and Facebook have become a significant testing ground for Artificial Intelligence developers who build programs, known as socialbots, that imitate human users by automating their social network activities such as forming social links and posting content. Particularly, Twitter users have shown difficulties in distinguishing these socialbots from the human users in their social graphs. Frequently, socialbots are effective in acquiring human users as followers and exercising influence within them. While the success of socialbots is certainly a remarkable achievement for AI practitioners, their proliferation in the Twitter sphere opens many possibilities for cybercrime. The proliferation of socialbots in Twitter motivates us to assess the characteristics or strategies that make socialbots most likely to succeed. In this direction, we created 120 socialbot accounts in Twitter, which have a profile, follow other users, and generate tweets either by reposting others' tweets or by generating their own synthetic tweets. Then, we employ a $2^k$$2k factorial design experiment to quantify the infiltration performance of different socialbot strategies, and examine the effectiveness of individual profile and activity related attributes of the socialbots. Our analysis is the first of a kind, and reveals what strategies make socialbots successful in the Twitter sphere.
Social Network Analysis and Mining