  Building on current work on multilingual hate speech (e.g., Ousidhoum et al.
(2019)) and hate speech reduction (e.g., Sap et al. (2020)), we present
XTREMESPEECH, a new hate speech dataset containing 20,297 social media passages
from Brazil, Germany, India and Kenya. The key novelty is that we directly
involve the affected communities in collecting and annotating the data - as
opposed to giving companies and governments control over defining and
combatting hate speech. This inclusive approach results in datasets more
representative of actually occurring online speech and is likely to facilitate
the removal of the social media content that marginalized communities view as
causing the most harm. Based on XTREMESPEECH, we establish novel tasks with
accompanying baselines, provide evidence that cross-country training is
generally not feasible due to cultural differences between countries and
perform an interpretability analysis of BERT's predictions.
