In recent debates on offensive language in participatory online spaces, the term ‘hate speech’ has become especially prominent. Originating from a legal context, the term usually refers to violent threats or expressions of prejudice against particular groups on the basis of race, religion, or sexual orientation. However, due to its explicit reference to the emotion of hate, it is also used more colloquially as a general label for any kind of negative expression. This ambiguity leads to misunderstandings in discussions about hate speech and challenges its identification. To meet this challenge, this article provides a modularized framework to differentiate various forms of hate speech and offensive language. On the basis of this framework, we present a text annotation study of 5,031 user comments on the topic of immigration and refuge posted in March 2019 on three German news sites, four Facebook pages, 13 YouTube channels, and one right-wing blog. An in-depth analysis of these comments identifies various types of hate speech and offensive language targeting immigrants and refugees. By exploring typical combinations of labeled attributes, we empirically map the variety of offensive language in the subject area ranging from insults to calls for hate crimes, going beyond the common ‘hate/no-hate’ dichotomy found in similar studies. The results are discussed with a focus on the grey area between hate speech and offensive language.
How well do hate speech, toxicity, abusive and offensive language classification models generalize across datasets?
