  The Covid-19 pandemic has caused a dramatic and parallel rise in dangerous
misinformation, denoted an `infodemic' by the CDC and WHO. Misinformation tied
to the Covid-19 infodemic changes continuously; this can lead to performance
degradation of fine-tuned models due to concept drift. Degredation can be
mitigated if models generalize well-enough to capture some cyclical aspects of
drifted data. In this paper, we explore generalizability of pre-trained and
fine-tuned fake news detectors across 9 fake news datasets. We show that
existing models often overfit on their training dataset and have poor
performance on unseen data. However, on some subsets of unseen data that
overlap with training data, models have higher accuracy. Based on this
observation, we also present KMeans-Proxy, a fast and effective method based on
K-Means clustering for quickly identifying these overlapping subsets of unseen
data. KMeans-Proxy improves generalizability on unseen fake news datasets by
0.1-0.2 f1-points across datasets. We present both our generalizability
experiments as well as KMeans-Proxy to further research in tackling the fake
news problem.
