HICC : a dataset for German hate speech in conversational context

We present HICC: A dataset of German Hate speech comments In Conversational Context. The dataset comprises the target hate speech comments to be detected, the root post that starts the conversation, and all direct replies to the root that were published before the target. We especially focus on comments that are difficult to identify as hate speech in isolation, but need context for classification. We show that LLM-based classification can successfully incorporate conversational context to identify such comments that do not explicitly contain hateful language. Our dataset is gathered from the social media platform X (formerly Twitter), manually annotated, and made freely available for research.

Authors

Publication Details

Journal
Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences)
Published
2025-09-01
DOI
https://doi.org/10.21256/zhaw-35860
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

HICC : a dataset for German hate speech in conversational context

Mark Cieliebak, Pius von Däniken, Katja Girschik, Lars Schmid et al.
Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences)
Hate Speech and Cyberbullying Detection
article

HICC : a dataset for German hate speech in conversational context

Mark Cieliebak, Pius von Däniken, Katja Girschik, Lars Schmid, Dirk Baier, Patrick Giedemann, Judith Bühler, Don Tuggener, Maria Kamenowski
article en

Abstract

We present HICC: A dataset of German Hate speech comments In Conversational Context. The dataset comprises the target hate speech comments to be detected, the root post that starts the conversation, and all direct replies to the root that were published before the target. We especially focus on comments that are difficult to identify as hate speech in isolation, but need context for classification. We show that LLM-based classification can successfully incorporate conversational context to identify such comments that do not explicitly contain hateful language. Our dataset is gathered from the social media platform X (formerly Twitter), manually annotated, and made freely available for research.

Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences)
Openalex Percentile: Top 13%
Hate Speech and Cyberbullying Detection
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