This study investigates the dynamics of anti-Muslim hate speech within Norwegian social media during the period between 2010 and 2021. Using a dataset of more than one million comments from Twitter and Facebook, we developed a custom hate speech classifier trained on an annotated corpus of 3,277 comments in Norwegian language. We identify that despite representing a small share of the total comments, hate speech content has increased over time. In an effort to understand the social network characteristics of hate speech content, we delve deeper into Twitter conversations as we can more easily identify how this content is spread. We develop network metrics to assess the prevalence, distribution, and diffusion of hateful content. The findings reveal that regardless of the number of users or tweets in a conversation, the volume of hateful content tends to remain constant. Furthermore, a small fraction of users contribute disproportionately to the dissemination of hate speech, with most conversations being limited in participant diversity. These results contribute to the growing field of computational social science by offering a novel methodology for studying hate speech in under-resourced languages and suggesting that mitigating hate speech may be possible through targeted network interventions rather than content removal alone.
Kasahara et al. (Fri,) studied this question.