# BoostNet: Bootstrapping detection of socialbots, and a case study from Guatemala

## TL;DR

BoostNet introduces a data-driven method to detect socialbots by reconstructing networks and analyzing Botometer scores, successfully identifying over 5,000 socialbots in a Guatemala case study.

## Contribution

The paper presents a novel network reconstruction and clustering approach for socialbot detection using minimal input and Botometer score analysis.

## Key findings

- Identified over 5,000 socialbots in Guatemala
- Developed a threshold inference method for socialbot detection
- Demonstrated effectiveness of the approach in a real-world case study

## 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.