Identifying Correlated Bots in Twitter

We develop a technique to identify abnormally correlated user accounts in Twitter, which are very unlikely to be human operated. This new approach of bot detection considers cross-correlating user activities and requires no labeled data, as opposed to existing bot detection techniques that consider users independently, and require large amount of recently labeled data. Our system uses a lag-sensitive hashing technique and a warping-invariant correlation measure to quickly organize the user accounts in clusters of abnormally correlated accounts. Our method is 94% precise and detects unique bots that other methods cannot detect. Our system produces daily reports on bots at a rate of several hundred bots per day. The reports are available online for further analysis.