  We study the selection of transfer languages for automatic abusive language
detection. Instead of preparing a dataset for every language, we demonstrate
the effectiveness of cross-lingual transfer learning for zero-shot abusive
language detection. This way we can use existing data from higher-resource
languages to build better detection systems for low-resource languages. Our
datasets are from seven different languages from three language families. We
measure the distance between the languages using several language similarity
measures, especially by quantifying the World Atlas of Language Structures. We
show that there is a correlation between linguistic similarity and classifier
performance. This discovery allows us to choose an optimal transfer language
for zero shot abusive language detection.
