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

Autor:	Richards, E. I. Velazquez, Gallagher, E., Suárez-Serrato, P.
Rok vydání:	2019
Předmět:	Computer Science - Computers and Society Computer Science - Social and Information Networks
Zdroj:	Selected Contributions on Statistics and Data Science in Latin America. FNE 2018. Springer Proceedings in Mathematics & Statistics, vol 301
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-030-31551-1_11
Popis:	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. Comment: 7 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1901.04542 Zobrazit plný text záznamu View this record from Arxiv