Causal diffused graph-transformer network with stacked early classification loss for efficient stream classification of rumours

Abstract

The growth in social media has led to the increasing spread of unverified or false information. Automatically detecting rumours and accessing their veracity, i.e., false rumours, true rumours, or unverified rumours, is an important and challenging task in social media analytics. This paper aims to build an effective and scalable stream classification framework for early fine-grained rumour classification based on community response. We propose a Causal Diffused Graph-Transformer Network (CDGTN) to extract features from the source-reply graph in a social media conversation. Then, we propose Source-Guided Incremental Attention Pooling (SGIAP) to aggregate the encoded features with discrete timestamps. To improve the performance of early classification, we propose a Stacked Early Classification Loss (SecLoss), which aims to minimize the classification loss over the time instances. This can greatly improve the effectiveness of early classification of rumours. To improve the efficiency of streaming rumour verification, we propose a continued inference algorithm based on prefix-sum, which can greatly reduce the computational complexity of stream classification of rumours. Furthermore, we annotated the first Chinese rumour verification dataset, by extending the existing Chinese-Twitter dataset, namely CR-Twitter, originally for rumour detection. We conducted experiments on the Twitter15, Twitter16, PHEME, Weibo, and the extended CR-Twitter datasets for rumour classification, to verify our proposed stream classification framework. The experimental results show that our proposed framework can significantly boost the effectiveness and efficiency of early stream classification of rumours. Models and datasets are released at: https://thcheung.github.io/cdgtn/.