# Introducing an Abusive Language Classification Framework for Telegram to Investigate the German Hater Community

arXiv: https://arxiv.org/abs/2109.07346 · HTML: https://mlsanity.com/paper/2109.07346/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [German's Next Language Model](https://mlsanity.com/paper/2010.10906/) (2020)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017)
- [Characterizing and Detecting Hateful Users on Twitter](https://mlsanity.com/paper/1803.08977/) (2018)
- [The Pushshift Telegram Dataset](https://mlsanity.com/paper/2001.08438/) (2020)

## Built on by (most cited first, 6 of 6)

- [Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation](https://mlsanity.com/paper/2205.00501/) (2022)
- [HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter](https://mlsanity.com/paper/2411.15462/) (2024)
- [More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram](https://mlsanity.com/paper/2410.08642/) (2024)
- [Mapping the Italian Telegram Ecosystem: Communities, Toxicity, and Hate Speech](https://mlsanity.com/paper/2504.19594/) (2025)
- [Characterization of Political Polarized Users Attacked by Language Toxicity on Twitter](https://mlsanity.com/paper/2407.12471/) (2024)
- [Toxic comments reduce the activity of volunteer editors on Wikipedia](https://mlsanity.com/paper/2304.13568/) (2023)

## Abstract (arXiv)

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.