  Since traditional social media platforms continue to ban actors spreading
hate speech or other forms of abusive languages (a process known as
deplatforming), these actors migrate to alternative platforms that do not
moderate users content. One popular platform relevant for the German hater
community is Telegram for which limited research efforts have been made so far.
This study aims to develop a broad framework comprising (i) an abusive language
classification model for German Telegram messages and (ii) a classification
model for the hatefulness of Telegram channels. For the first part, we use
existing abusive language datasets containing posts from other platforms to
develop our classification models. For the channel classification model, we
develop a method that combines channel-specific content information collected
from a topic model with a social graph to predict the hatefulness of channels.
Furthermore, we complement these two approaches for hate speech detection with
insightful results on the evolution of the hater community on Telegram in
Germany. We also propose methods for conducting scalable network analyses for
social media platforms to the hate speech research community. As an additional
output of this study, we provide an annotated abusive language dataset
containing 1,149 annotated Telegram messages.

Computation and Language