  Leveraging unlabelled data through weak or distant supervision is a
compelling approach to developing more effective text classification models.
This paper proposes a simple but effective data augmentation method, which
leverages the idea of pseudo-labelling to select samples from noisy distant
supervision annotation datasets. The result shows that the proposed method
improves the accuracy of biased news detection models.
