Its all in a name: detecting and labeling bots by their name

  • David M. BeskowEmail author
  • Kathleen M. Carley
S.I. : SBP-BRiMS 2018


Automated social media bots have existed almost as long as the social media environments they inhabit. Their emergence has triggered numerous research efforts to develop increasingly sophisticated means to detect these accounts. These efforts have resulted in a cat and mouse cycle in which detection algorithms evolve trying to keep up with ever evolving bots. As part of this continued evolution, our research proposes a multi-model ‘tool-box’ approach in order to conduct detection at various tiers of data granularity. To support this toolbox approach this research also uses random string detection applied to user names to filter twitter streams for bot accounts and use this as labeled training data for follow on research.


Bot detection Machine learning Random string detection Disinformation Fake news 



This work was supported in part by the Office of Naval Research (ONR) Multidisciplinary University Research Initiative Award N000140811186 and Award N000141812108, the Army Research Laboratory Award W911NF1610049, Defense Threat Reductions Agency Award HDTRA11010102, and the Center for Computational Analysis of Social and Organization Systems (CASOS). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ONR, ARL, DTRA, or the U.S. government.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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