  We present Stanceosaurus, a new corpus of 28,033 tweets in English, Hindi,
and Arabic annotated with stance towards 251 misinformation claims. As far as
we are aware, it is the largest corpus annotated with stance towards
misinformation claims. The claims in Stanceosaurus originate from 15
fact-checking sources that cover diverse geographical regions and cultures.
Unlike existing stance datasets, we introduce a more fine-grained 5-class
labeling strategy with additional subcategories to distinguish implicit stance.
Pre-trained transformer-based stance classifiers that are fine-tuned on our
corpus show good generalization on unseen claims and regional claims from
countries outside the training data. Cross-lingual experiments demonstrate
Stanceosaurus' capability of training multi-lingual models, achieving 53.1 F1
on Hindi and 50.4 F1 on Arabic without any target-language fine-tuning.
Finally, we show how a domain adaptation method can be used to improve
performance on Stanceosaurus using additional RumourEval-2019 data. We make
Stanceosaurus publicly available to the research community and hope it will
encourage further work on misinformation identification across languages and
cultures.
