  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.
