#BotOrNot

In recent years, Twitter, a social networking website, has been affected by a steady rise in spam on its network. Hijacking of social media accounts has become a modern-day danger. Motivations for this can range from attempts in identity theft to simply skewing the perception of an audience. In this paper, we extend our previous work, Engineering Your Social Network to Detect Fraudulent Profiles, by doing an investigation of spam bots on Twitter. We propose an algorithm that will distinguish a spam bot, from a genuine user account by using a JavaScript testing framework that consumes Twitter's REST API. We ran a dataset of 700 Twitter accounts through our algorithm and identified that roughly 11% of the dataset were bot