  The ongoing COVID-19 pandemic has caused immeasurable losses for people
worldwide. To contain the spread of the virus and further alleviate the crisis,
various health policies (e.g., stay-at-home orders) have been issued which
spark heated discussions as users turn to share their attitudes on social
media. In this paper, we consider a more realistic scenario on stance detection
(i.e., cross-target and zero-shot settings) for the pandemic and propose an
adversarial learning-based stance classifier to automatically identify the
public's attitudes toward COVID-19-related health policies. Specifically, we
adopt adversarial learning that allows the model to train on a large amount of
labeled data and capture transferable knowledge from source topics, so as to
enable generalize to the emerging health policies with sparse labeled data. To
further enhance the model's deeper understanding, we incorporate policy
descriptions as external knowledge into the model. Meanwhile, a GeoEncoder is
designed which encourages the model to capture unobserved background factors
specified by each region and then represent them as non-text information. We
evaluate the performance of a broad range of baselines on the stance detection
task for COVID-19-related health policies, and experimental results show that
our proposed method achieves state-of-the-art performance in both cross-target
and zero-shot settings.
