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