An Adversarial Benchmark for Fake News Detection Models. (arXiv:2201.00912v1 [cs.CL])

With the proliferation of online misinformation, fake news detection has
gained importance in the artificial intelligence community. In this paper, we
propose an adversarial benchmark that tests the ability of fake news detectors
to reason about real-world facts. We formulate adversarial attacks that target
three aspects of "understanding": compositional semantics, lexical relations,
and sensitivity to modifiers. We test our benchmark using BERT classifiers
fine-tuned on the LIAR arXiv:arch-ive/1705648 and Kaggle Fake-News datasets,
and show that both models fail to respond …

arxiv detection fake fake news news