  The popularity of social media platforms such as Twitter has led to the
proliferation of automated bots, creating both opportunities and challenges in
information dissemination, user engagements, and quality of services. Past
works on profiling bots had been focused largely on malicious bots, with the
assumption that these bots should be removed. In this work, however, we find
many bots that are benign, and propose a new, broader categorization of bots
based on their behaviors. This includes broadcast, consumption, and spam bots.
To facilitate comprehensive analyses of bots and how they compare to human
accounts, we develop a systematic profiling framework that includes a rich set
of features and classifier bank. We conduct extensive experiments to evaluate
the performances of different classifiers under varying time windows, identify
the key features of bots, and infer about bots in a larger Twitter population.
Our analysis encompasses more than 159K bot and human (non-bot) accounts in
Twitter. The results provide interesting insights on the behavioral traits of
both benign and malicious bots.
