  The widespread use of offensive content in social media has led to an
abundance of research in detecting language such as hate speech, cyberbullying,
and cyber-aggression. Recent work presented the OLID dataset, which follows a
taxonomy for offensive language identification that provides meaningful
information for understanding the type and the target of offensive messages.
However, it is limited in size and it might be biased towards offensive
language as it was collected using keywords. In this work, we present SOLID, an
expanded dataset, where the tweets were collected in a more principled manner.
SOLID contains over nine million English tweets labeled in a semi-supervised
fashion. We demonstrate that using SOLID along with OLID yields sizable
performance gains on the OLID test set for two different models, especially for
the lower levels of the taxonomy.
