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With the rise of social media, a rise of hateful content can be observed.Even though the understanding and definitions of hate speech varies, platforms,communities, and legislature all acknowledge the problem. Therefore,adolescents are a new and active group of social media users. The majority ofadolescents experience or witness online hate speech. Research in the field ofautomated hate speech classification has been on the rise and focuses onaspects such as bias, generalizability, and performance. To increasegeneralizability and performance, it is important to understand biases withinthe data. This research addresses the bias of youth language within hate speechclassification and contributes by providing a modern and anonymized hate speechyouth language data set consisting of 88.395 annotated chat messages. The dataset consists of publicly available online messages from the chat platformDiscord. ~6,42% of the messages were classified by a self-developed annotationschema as hate speech. For 35.553 messages, the user profiles provided ageannotations setting the average author age to under 20 years old.

Hateful Messages: A Conversational Data Set of Hate Speech produced by Adolescents on Discord