Dehumanization is a pernicious process of denying some or all attributes of humanness to the target group. It is frequently cited as a common hallmark of incitement to commit genocide. The international security landscape has seen a dramatic shift following the 2022 Russian invasion of Ukraine. This, coupled with recent developments in the conceptualization of dehumanization, necessitates the creation of new techniques for analyzing and detecting this extreme violence-related phenomenon on a large scale. Our project pioneers the development of a detection system for instances of dehumanization. To achieve this, we collected the entire posting history of the most popular bloggers on Russian Telegram and tested classical machine learning, deep learning, and zero-shot learning approaches to explore and detect the dehumanizing rhetoric. We found that the transformer-based method for entity extraction SpERT shows a promising result of F 1 = 0.85 for binary classification. The proposed methods can be built into the systems of anticipatory governance, contribute to the collection of evidence of genocidal intent in the Russian invasion of Ukraine, and pave the way for large-scale studies of dehumanizing language. This paper contains references to language that some readers may find offensive.