  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.
