The coming age of adversarial social bot detection

Authors

  • Stefano Cresci Institute of Informatics and Telematics, IIT-CNR, Pisa, Italy
  • Marinella Petrocchi Institute of Informatics and Telematics, IIT-CNR, Pisa, Italy
  • Angelo Spognardi CS Dept., Sapienza University of Rome
  • Stefano Tognazzi Computer and Information Science Department, Konstanz University, Konstanz, Germany

DOI:

https://doi.org/10.5210/fm.v26i7.11474

Keywords:

data mining, bot detection, Social science methods or tools

Abstract

Social bots are automated accounts often involved in unethical or illegal activities. Academia has shown how these accounts evolve over time, becoming increasingly smart at hiding their true nature by disguising themselves as genuine accounts. If they evade, bots hunters adapt their solutions to find them: the cat and mouse game. Inspired by adversarial machine learning and computer security, we propose an adversarial and proactive approach to social bot detection, and we call scholars to arms, to shed light on this open and intriguing field of study.

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Published

2021-05-10

How to Cite

Cresci, S., Petrocchi, M., Spognardi, A., & Tognazzi, S. (2021). The coming age of adversarial social bot detection. First Monday, 26(7). https://doi.org/10.5210/fm.v26i7.11474