The presence of offensive language on social media platforms and the implications this poses is becoming a major concern in modern society. Given the enormous amount of content created every day, automatic methods are required to detect and deal with this type of content. Until now, most of the research has focused on solving the problem for the English language, while the problem is multilingual. We construct a Danish dataset containing user-generated comments from \textit{Reddit} and \textit{Facebook}. It contains user generated comments from various social media platforms, and to our knowledge, it is the first of its kind. Our dataset is annotated to capture various types and target of offensive language. We develop four automatic classification systems, each designed to work for both the English and the Danish language. In the detection of offensive language in English, the best performing system achieves a macro averaged F1-score of $0.74$, and the best performing system for Danish achieves a macro averaged F1-score of $0.70$. In the detection of whether or not an offensive post is targeted, the best performing system for English achieves a macro averaged F1-score of $0.62$, while the best performing system for Danish achieves a macro averaged F1-score of $0.73$. Finally, in the detection of the target type in a targeted offensive post, the best performing system for English achieves a macro averaged F1-score of $0.56$, and the best performing system for Danish achieves a macro averaged F1-score of $0.63$. Our work for both the English and the Danish language captures the type and targets of offensive language, and present automatic methods for detecting different kinds of offensive language such as hate speech and cyberbullying.

翻译：社交媒体平台中攻击性语言的存在及其带来的影响正成为现代社会的一个主要关切。鉴于每天产生的大量内容，需要自动方法来检测和处理这类内容。迄今为止，大多数研究都集中在解决英语问题，而这一问题实际上是多语言的。我们构建了一个丹麦语数据集，其中包含来自 \textit{Reddit} 和 \textit{Facebook} 的用户生成评论。该数据集包含来自各种社交媒体平台的用户生成评论，据我们所知，这是首个此类数据集。我们的数据集经过标注，以捕捉攻击性语言的不同类型和对象。我们开发了四个自动分类系统，每个系统都设计为同时适用于英语和丹麦语。在英语攻击性语言检测中，最佳系统实现了宏平均 F1 分数 $0.74$，而丹麦语的最佳系统实现了宏平均 F1 分数 $0.70$。在检测攻击性帖子是否具有针对性时，英语最佳系统实现了宏平均 F1 分数 $0.62$，而丹麦语最佳系统实现了宏平均 F1 分数 $0.73$。最后，在检测针对性攻击帖子中的目标类型时，英语最佳系统实现了宏平均 F1 分数 $0.56$，而丹麦语最佳系统实现了宏平均 F1 分数 $0.63$。我们针对英语和丹麦语的工作捕捉了攻击性语言的类型和目标，并提出了自动方法来检测不同类型的攻击性语言，如仇恨言论和网络欺凌。