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.

社交媒体上表达仇恨内容的数量不断上升，年轻人在这方面的受影响较为明显，因此研究自动化仇恨言论分类对于理解数据中的偏见十分重要。本研究提供了一个现代化且匿名的青少年语言数据集，其中包括了88,395条带有注释的聊天消息，以提高分类的普适性和性能。

仇恨言论：一种由Discord上的青少年产生的对话数据集