Are some Twitter users more naturally predisposed to interacting with social bots and can social bot creators exploit this knowledge to increase the odds of getting a response? Social bots are growing more intelligent, moving beyond simple reposts of boilerplate ad content to attempt to engage with users and then exploit this trust to promote a product or agenda. While much research has focused on how to identify such bots in the process of spam detection, less research has looked at the other side of the question—detecting users likely to be fooled by bots. This talk provides a summary of research and developments in the social bots arms race before sharing results of our experiment examining user susceptibility. We find that a users' Klout score, friends count, and followers count are most predictive of whether a user will interact with a bot, and that the Random Forest algorithm produces the best classifier, when used in conjunction with appropriate feature ranking algorithms. With this knowledge, social bot creators could significantly reduce the chance of targeting users who are unlikely to interact. Users displaying higher levels of extroversion were more likely to interact with our social bots. This may have implications for eLearning based awareness training as users higher in extraversion have been shown to perform better when they have greater control of the learning environment. Overall, these results show promise for helping understand which users are most vulnerable to social bots. Chris Sumner (@thesuggmeister) is a co-founder of the not-for-profit Online Privacy Foundation who actively participate in and contribute to the emerging discipline of Social Media Behavioral Residue research. Chris has previously spoken on this area of research at conferences including BlackHat, DEF CON, 44CON, the European Conference on Personality and the International Conference on Machine Learning and Applications. Randall Wald is a postdoctoral researcher investigating data mining and machine learning at Florida Atlantic University. Following his BS in Biology from the California Institute of Technology, Randall chose to shift his focus to computer science, applying his domain knowledge towards bioinformatics and building models to predict disease. He also studies machine learning for other domains, including machine condition monitoring, software engineering, and social networking.