  A major concern with text-based news veracity detection methods is that they
may not generalize across countries and cultures. In this short paper, we
explicitly test news veracity models across news data from the United States
and the United Kingdom, demonstrating there is reason for concern of
generalizabilty. Through a series of testing scenarios, we show that text-based
classifiers perform poorly when trained on one country's news data and tested
on another. Furthermore, these same models have trouble classifying unseen,
unreliable news sources. In conclusion, we discuss implications of these
results and avenues for future work.
