How Many Bots in Russian Troll Tweets?

Increased usage of bots through the Internet in general, and social networks in particular, has many implications related to influencing public opinion. Mechanisms to distinguish humans from machines span a broad spectrum of applications and hence vary in their nature and complexity. Here we use several public Twitter datasets to build a model that can predict whether or not an account is a bot account based on features extracted at the tweet or the account level. We then apply the model to Twitter's Russian Troll Tweets dataset. At the account level, we evaluate features related to how often Twitter accounts are tweeting, as previous research has shown that bots are very active at some account levels and very low at others. At the tweet level, we noticed that bot accounts tend to sound more formal or structured, whereas real user accounts tend to be more informal in that they contain more slang, slurs, cursing, and the like. We also noted that bots can be created for a range of different goals (e.g., marketing and politics) and that their behaviors vary based on those distinct goals. Ultimately, for high bot-prediction accuracy, models should consider and distinguish among the different goals for which bots are created.