Bot or Not? Deciphering Time Maps for Tweet Interarrivals

This exploratory study used the R Statistical Software to perform Monte Carlo simulation of time maps, which characterize events based on the elapsed time since the last event and the time that will transpire until the next event, and compare them to time maps from real Twitter users. Time maps are used to explore differences in the interarrival patterns of Tweets between human users, humans who use scheduling services like TweetDeck and HootSuite, and non-human (“bot”) users. The results indicate that there are differences between the tweet interarrival patterns across these categories of users, and that time maps could potentially be used to automate the detection of bot accounts on Twitter. This could enhance social media intelligence capabilities, help bot developers build more “human-like” Twitter bots to avoid detection, or both.