Taking a Stance on Fake News: Towards Automatic Disinformation Assessment via Deep Bidirectional Transformer Language Models for Stance Detection
Table 2: Performance of various methods on the FNC-I benchmark. The first and second groups are methods introduced during and after the challenge period, respectively. Best results are in bold.
['Method', '[ITALIC] Accw', 'Acc']
Results of our proposed method, the top three methods in the original Fake News Challenge, and the best-performing methods since the challenge’s conclusion on the FNC- A confusion matrix for our method is presented in the Appendix. To the best of our knowledge, our method achieves state-of-the-art results in weighted-accuracy and standard accuracy on the dataset. Notably, since the conclusion of the Fake News Challenge in 2017, the weighted-accuracy error-rate has decreased by 8%, signifying improved performance of NLP models and innovations in the domain of stance detection, as well as a continued interest in combating the spread of disinformation.