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.
Cite
@article{arxiv.1901.04542,
title = {BoostNet: Bootstrapping detection of socialbots, and a case study from Guatemala},
author = {E. I. Velazquez Richards and E. Gallagher and P. Suárez-Serrato},
journal= {arXiv preprint arXiv:1901.04542},
year = {2019}
}