  Public figures receive a disproportionate amount of abuse on social media,
impacting their active participation in public life. Automated systems can
identify abuse at scale but labelling training data is expensive, complex and
potentially harmful. So, it is desirable that systems are efficient and
generalisable, handling both shared and specific aspects of online abuse. We
explore the dynamics of cross-group text classification in order to understand
how well classifiers trained on one domain or demographic can transfer to
others, with a view to building more generalisable abuse classifiers. We
fine-tune language models to classify tweets targeted at public figures across
DOmains (sport and politics) and DemOgraphics (women and men) using our novel
DODO dataset, containing 28,000 labelled entries, split equally across four
domain-demographic pairs. We find that (i) small amounts of diverse data are
hugely beneficial to generalisation and model adaptation; (ii) models transfer
more easily across demographics but models trained on cross-domain data are
more generalisable; (iii) some groups contribute more to generalisability than
others; and (iv) dataset similarity is a signal of transferability.

Computation and Language;Computers and Society