  With the increasing use of machine-learning driven algorithmic judgements, it
is critical to develop models that are robust to evolving or manipulated
inputs. We propose an extensive analysis of model robustness against linguistic
variation in the setting of deceptive news detection, an important task in the
context of misinformation spread online. We consider two prediction tasks and
compare three state-of-the-art embeddings to highlight consistent trends in
model performance, high confidence misclassifications, and high impact
failures. By measuring the effectiveness of adversarial defense strategies and
evaluating model susceptibility to adversarial attacks using character- and
word-perturbed text, we find that character or mixed ensemble models are the
most effective defenses and that character perturbation-based attack tactics
are more successful.
