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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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BoTox-Dataset for Classifying German Hate Speech Comments with Criminal Relevance

Authors: Kums, Vincent; Meyer, Florian; Pivit, Luisa Emily; Vedenina, Uliana; Wortmann, Jonas; Siegel, Melanie; Labudde, Dirk;

BoTox-Dataset for Classifying German Hate Speech Comments with Criminal Relevance

Abstract

This repository contains the BoTox dataset, a German dataset for analyzing offensive language and conversations in the context of criminal relevance under the German Criminal Code (StGB). It was created as part of the BoTox research project (https://botox.h-da.de/) in 2025, which aimed to investigate hate speech in a criminal law context and to detect hate speech bots. Content public dataset with comment texts as csv annotation guidelines (original German version) About the Dataset This is a German dataset on offensive language, annotated according to three predefined classes, each with several paragraphs from German criminal law. In total, the dataset contains 1,190 annotated comments from various sources. The data was compiled in 2025 and annotated by three teams of annotators after intensive training by the Frankfurt am Main Public Prosecutor's Office. The dataset contains annotations according to inter-annotator agreement. Further details can be found in the accompanying paper. Description of the columns Column Name Description index rolling index text comment text class_1 binary annotation class_2 binary annotation class_3 binary annotation class_0 binary annotation output textual annotation Citation If you use the dataset, please cite our respective paper A Novel Dataset for Classifying German Hate Speech Comments with Criminal Relevance", which was presented on the on the 9th Workshop on Online Abuse and Harms (WOAH) at August 1, 2025 as part of the ACL conference. ACL-Style:Vincent Kums, Florian Meyer, Luisa Pivit, Uliana Vedenina, Jonas Wortmann, Melanie Siegel and Dirk Labudde A Novel Dataset for Classifying German Hate Speech Comments with Criminal Relevance. In Proceedings of the 9th Workshop on Online Abuse and Harms (WOAH), pages 41-52, Vienna, Austria. Association for Computational Linguistics. BibTeX: @inproceedings{kums-etal-2025-novel, title = "A Novel Dataset for Classifying {G}erman Hate Speech Comments with Criminal Relevance", author = "Kums, Vincent and Meyer, Florian and Pivit, Luisa and Vedenina, Uliana and Wortmann, Jonas and Siegel, Melanie and Labudde, Dirk", editor = "Calabrese, Agostina and de Kock, Christine and Nozza, Debora and Plaza-del-Arco, Flor Miriam and Talat, Zeerak and Vargas, Francielle", booktitle = "Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH)", month = aug, year = "2025", address = "Vienna, Austria", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2025.woah-1.4/", pages = "41--52", ISBN = "979-8-89176-105-6" }

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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