Online Human-Bot Interactions: Detection, Estimation, and Characterization

Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet content and sentiment, network patterns, and activity time series. We benchmark the classification framework by using a publicly available dataset of Twitter bots. This training data is enriched by a manually annotated collection of active Twitter users that include both humans and bots of varying sophistication. Our models yield high accuracy and agreement with each other and can detect bots of…

• NS
  National Science Foundation

  Awards: 1101743, CCF-1101743
• JS
  James S. McDonnell Foundation
• DA
  Defense Advanced Research Projects Agency

  Award: W911NF-12-1-0037
• OO
  Office of Naval Research

  Award: N15A-020-0053