Which Users Reply to and Interact with Twitter Social Bots?

Abstract

Bots, autonomous programs which attempt to imitate human behavior, are becoming more of an issue on Twitter as the popular social network becomes an important target for spammers and others who wish to guide publicsentiment. While much research has attempted to discover andfilter out bots, less work has focused on the properties of those most susceptible to bots. In this study, we examine data from 610 users who were contacted by a bot and who could choose to either reply to or follow the bot, in order to discover which traits lead to replies alone or to interaction (replies or follows). In particular, we use feature ranking algorithms to order thedifferent traits (features) in terms of their relevance to thetwo classes ("interacted with bot" and "replied to bot"), andthen both consider those features which are frequently selectedby many different algorithms as well as use these features tobuild classification models. This is the first study to considerthe features which can influence both of these classes, and inparticular note how the chosen features differ between the twoclasses. We found that while these two classes have much incommon (for example, users with high Klout.com scores aremore likely to both reply to and interact with bots), therewere notable differences between them both in terms of whichfeatures are most important and which algorithms can be usedto build the best models. We also found that overall, the bestclassification models built with feature selection outperformedthe best models without feature selection, but the optimalchoices of feature ranker, learner, and feature subset size werenecessary to achieve this performance.