Can We Identify Stance Without Target Arguments? A Study for Rumour Stance Classification
This work addresses rumour stance classification for social media analysis, but it is incremental as it builds on existing methods to improve target-aware reasoning.
The study tackled the problem of rumour stance classification by showing that many replies in datasets allow stance inference without the target, and proposed a framework that enhances reasoning with targets to achieve state-of-the-art performance on two benchmarks.
Considering a conversation thread, rumour stance classification aims to identify the opinion (e.g. agree or disagree) of replies towards a target (rumour story). Although the target is expected to be an essential component in traditional stance classification, we show that rumour stance classification datasets contain a considerable amount of real-world data whose stance could be naturally inferred directly from the replies, contributing to the strong performance of the supervised models without awareness of the target. We find that current target-aware models underperform in cases where the context of the target is crucial. Finally, we propose a simple yet effective framework to enhance reasoning with the targets, achieving state-of-the-art performance on two benchmark datasets.