Claudia Wagner, et al., report that with the increasing importance of online social networks such as Twitter or Facebook, a new breed of computer programs, so-called social bots, emerged. Social bots are automatic or semi-automatic computer programs that mimic humans and or human behavior in online social networks. Social bots can attack users in online social networks to pursue a variety of latent goals, such as to spread information or to influence targets. Without a deep understanding of the nature of such attacks or the susceptibility of users, the potential of social media as an instrument for facilitating discourse or democratic processes is in jeopardy. In this paper, they investigated data from the Social Bot Challenge 2011, an experiment conducted by the Web Ecology Project during 2011, in which three teams implemented a number of social bots that aimed to influence user behavior on Twitter.
Using this data, they developed models to i) identify susceptible users among a set of targets, and ii) predict users level of susceptibility. They explored the predictiveness of three different groups of features (network, behavioral and linguistic features) for these tasks. Their results suggest that susceptible users tend to use Twitter for a conversational purpose and tend to be more open and social since they communicate with many different users, use more social words and show more affection than non-susceptible users. In total, they used 97 different features to first predict infections by training various classifiers and second aim to predict users level of susceptibility by using regression models. Thus, unlike previous research, their work does not focus on detecting social bots in online social networks, but on detecting users who are susceptible to their attacks.
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