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BoostNet: Bootstrapping Detection of Socialbots, and a Case Study from Guatemala

  • E. I. Velazquez Richards
  • E. Gallagher
  • P. Suárez-SerratoEmail author
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 301)

Abstract

We present a method to reconstruct networks of socialbots given minimal input. Then we use Kernel Density Estimates of Botometer scores from 47,000 social networking accounts to find clusters of automated accounts, discovering over 5,000 socialbots. This statistical and data-driven approach allows for inference of thresholds for socialbot detection, as illustrated in a case study we present from Guatemala.

Keywords

Kernel decomposition estimate Data analysis Social network analysis Empirical data 

Notes

Acknowledgements

We thank the OSoMe team in Indiana University for access to Botometer , and also Twitter for allowing access to data through their APIs. PSS acknowledges support from UNAM-DGAPA-PAPIIT-IN104819 and thanks IPAM, UCLA for an excellent and stimulating environment during the final stages of this work.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • E. I. Velazquez Richards
    • 1
  • E. Gallagher
    • 2
  • P. Suárez-Serrato
    • 1
    • 3
    Email author
  1. 1.Instituto de MatemáticasUniversidad Nacional Autónoma de México, Ciudad UniversitariaCoyoacán, Mexico CityMexico
  2. 2.Integrative MediaPursuance ProjectDallasUSA
  3. 3.Department of MathematicsUniversity of California Santa BarbaraGoletaUSA

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