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

TL;DR

This paper introduces a novel rumour detection approach on Twitter using graph neural networks combined with a targeted oversampling technique to address class imbalance, resulting in improved early detection performance.

Contribution

The study presents a new oversampling method based on contextual data augmentation and two GNN models for modeling conversations, enhancing rumour detection accuracy and early identification.

Findings

GNN models outperform state-of-the-art classifiers by over 20% in F1-score.

Oversampling improves model performance by more than 9%.

Focusing augmentation on relevant tweets further enhances results.

Abstract

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.