  Pre-trained language models (PLMs) are fundamental for natural language
processing applications. Most existing PLMs are not tailored to the noisy
user-generated text on social media, and the pre-training does not factor in
the valuable social engagement logs available in a social network. We present
TwHIN-BERT, a multilingual language model productionized at Twitter, trained on
in-domain data from the popular social network. TwHIN-BERT differs from prior
pre-trained language models as it is trained with not only text-based
self-supervision, but also with a social objective based on the rich social
engagements within a Twitter heterogeneous information network (TwHIN). Our
model is trained on 7 billion tweets covering over 100 distinct languages,
providing a valuable representation to model short, noisy, user-generated text.
We evaluate our model on various multilingual social recommendation and
semantic understanding tasks and demonstrate significant metric improvement
over established pre-trained language models. We open-source TwHIN-BERT and our
curated hashtag prediction and social engagement benchmark datasets to the
research community.
