Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks
Heng Wang, Wenqian Zhang, Yuyang Bai, Zhaoxuan Tan, Shangbin Feng,, Qinghua Zheng, Minnan Luo

TL;DR
This paper introduces MVSD, a multi-view framework utilizing external movie knowledge and user network data to improve spoiler detection accuracy in movie reviews.
Contribution
The authors create a large-scale dataset and a comprehensive movie knowledge base, and develop a novel heterogeneous graph neural network for spoiler detection.
Findings
MVSD outperforms existing spoiler detection methods.
External knowledge and user interactions significantly improve detection robustness.
The framework effectively models diverse data sources for accurate classification.
Abstract
Online movie review platforms are providing crowdsourced feedback for the film industry and the general public, while spoiler reviews greatly compromise user experience. Although preliminary research efforts were made to automatically identify spoilers, they merely focus on the review content itself, while robust spoiler detection requires putting the review into the context of facts and knowledge regarding movies, user behavior on film review platforms, and more. In light of these challenges, we first curate a large-scale network-based spoiler detection dataset LCS and a comprehensive and up-to-date movie knowledge base UKM. We then propose MVSD, a novel Multi-View Spoiler Detection framework that takes into account the external knowledge about movies and user activities on movie review platforms. Specifically, MVSD constructs three interconnecting heterogeneous information networks to…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Misinformation and Its Impacts · Spam and Phishing Detection
MethodsGraph Neural Network · Balanced Selection
