An Adversarial Benchmark for Fake News Detection Models
This addresses the need for more robust fake news detectors in combating online misinformation, but it is incremental as it builds on existing models and datasets.
The paper tackled the problem of fake news detection by proposing an adversarial benchmark to test models' ability to reason about real-world facts, showing that BERT classifiers fine-tuned on existing datasets fail to respond to changes in compositional and lexical meaning.
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 to changes in compositional and lexical meaning. Our results strengthen the need for such models to be used in conjunction with other fact checking methods.