On the use of text augmentation for stance and fake news detection

Data Augmentation (DA) aims at synthesizing new training instances by applying transformations to available ones. DA has several well-known benefits such as: (i) increasing generalization ability; (ii) preventing data scarcity; and (iii) helping resolve class imbalance issues. In this work, we investigate the use of DA for stance and fake news detection. In the first part of our work, we explore the effect of various DA techniques on the performance of common classification algorithms. Our study reveals that the motto 'the more, the better' is the wrong approach regarding text augmentation and that there is no one-size-fits-all text augmentation technique. The second part of our work leverages the results of our study to propose a novel augmentation-based, ensemble learning approach. The proposed approach leverages text augmentation to enhance base learners' diversity and accuracy, ergo the predictive performance of the ensemble. The third part of our work experimentally investigates the use of DA to cope with the class imbalance problem. Class imbalance is very common in stance and fake news detection and often results in biased models. In this work we show how and to what extent text augmentation can help resolving moderate and severe imbalance.