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Recently, online social media has become a primary source for new informationand misinformation or rumours. In the absence of an automatic rumour detectionsystem the propagation of rumours has increased manifold leading to serioussocietal damages. In this work, we propose a novel method for buildingautomatic rumour detection system by focusing on oversampling to alleviatingthe fundamental challenges of class imbalance in rumour detection task. Ouroversampling method relies on contextualised data augmentation to generatesynthetic samples for underrepresented classes in the dataset. The key ideaexploits selection of tweets in a thread for augmentation which can be achievedby introducing a non-random selection criteria to focus the augmentationprocess on relevant tweets. Furthermore, we propose two graph neuralnetworks(GNN) to model non-linear conversations on a thread. To enhance thetweet representations in our method we employed a custom feature selectiontechnique based on state-of-the-art BERTweet model. Experiments of threepublicly available datasets confirm that 1) our GNN models outperform the thecurrent state-of-the-art classifiers by more than 20%(F1-score); 2) ouroversampling technique increases the model performance by more than9%;(F1-score) 3) focusing on relevant tweets for data augmentation vianon-random selection criteria can further improve the results; and 4) ourmethod has superior capabilities to detect rumours at very early stage.

Rumour detection using graph neural network and oversampling in benchmark Twitter dataset