  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 of
adolescents experience or witness online hate speech. Research in the field of
automated hate speech classification has been on the rise and focuses on
aspects such as bias, generalizability, and performance. To increase
generalizability and performance, it is important to understand biases within
the data. This research addresses the bias of youth language within hate speech
classification and contributes by providing a modern and anonymized hate speech
youth language data set consisting of 88.395 annotated chat messages. The data
set consists of publicly available online messages from the chat platform
Discord. ~6,42% of the messages were classified by a self-developed annotation
schema as hate speech. For 35.553 messages, the user profiles provided age
annotations setting the average author age to under 20 years old.

Computation and Language