  Several machine learning-based spoiler detection models have been proposed
recently to protect users from spoilers on review websites. Although dependency
relations between context words are important for detecting spoilers, current
attention-based spoiler detection models are insufficient for utilizing
dependency relations. To address this problem, we propose a new spoiler
detection model called SDGNN that is based on syntax-aware graph neural
networks. In the experiments on two real-world benchmark datasets, we show that
our SDGNN outperforms the existing spoiler detection models.
