  Automatic fake news detection with machine learning can prevent the
dissemination of false statements before they gain many views. Several datasets
labeling statements as legitimate or false have been created since the 2016
United States presidential election for the prospect of training machine
learning models. We evaluate the robustness of both traditional and deep
state-of-the-art models to gauge how well they may perform in the real world.
We find that traditional models tend to generalize better to data outside the
distribution it was trained on compared to more recently-developed large
language models, though the best model to use may depend on the specific task
at hand.
