  Recently, online social media has become a primary source for new information
and misinformation or rumours. In the absence of an automatic rumour detection
system the propagation of rumours has increased manifold leading to serious
societal damages. In this work, we propose a novel method for building
automatic rumour detection system by focusing on oversampling to alleviating
the fundamental challenges of class imbalance in rumour detection task. Our
oversampling method relies on contextualised data augmentation to generate
synthetic samples for underrepresented classes in the dataset. The key idea
exploits selection of tweets in a thread for augmentation which can be achieved
by introducing a non-random selection criteria to focus the augmentation
process on relevant tweets. Furthermore, we propose two graph neural
networks(GNN) to model non-linear conversations on a thread. To enhance the
tweet representations in our method we employed a custom feature selection
technique based on state-of-the-art BERTweet model. Experiments of three
publicly available datasets confirm that 1) our GNN models outperform the the
current state-of-the-art classifiers by more than 20%(F1-score); 2) our
oversampling technique increases the model performance by more than
9%;(F1-score) 3) focusing on relevant tweets for data augmentation via
non-random selection criteria can further improve the results; and 4) our
method has superior capabilities to detect rumours at very early stage.
