Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks (2023)

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 spoiler detection model 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 model diverse data sources and their multi-view attributes, while we design and employ a novel heterogeneous graph neural network architecture for spoiler detection as node-level classification. Extensive experiments demonstrate that MVSD advances the state-of-the-art on two spoiler detection datasets, while the introduction of external knowledge and user interactions help ground robust spoiler detection.

## introduction movie review websites such as imdb foot_0 and rotten tomato foot_1 have become popular avenues for movie commentary, discussion, and recommendation @xcite . among user-generated movie reviews, some of them contain spoilers, ## resource curation we first curate a large-scale spoiler detection dataset lcs based on imdb, providing rich information such as review text, movie metadata, user activities, and more. motivated by the success of external knowledge in related tasks @xcite @xcite , we construct a comprehensive movie knowledge base ukm with important movie information and up-to-date entries. ## methodology we propose mvsd, a multi-view spoiler detection framework. the overall architecture of the model is illustrated in figure 2 . to leverage external movie knowledge and user activities that are essential in robust spoiler detection, mvsd constructs heterogeneous information net-works to jointly represent diverse information sources. specifically, we build three subgraphs: movie-review subgraph, user-review subgraph, and knowledge subgraph, each modeling one aspect of the spoiler detection process. mvsd first separately encodes the multi-view features of these subgraphs through heterogeneous gnns, then fuses the learned representations of the three subgraphs through subgraph interaction. mvsd conducts spoiler detection with a node classification setting based on the learned representations of review nodes. ## conclusion we make the case for incorporating external knowledge and user networks on movie review websites for robust and well-grounded spoiler detection. specifically, we curate lcs, the largest spoiler detection dataset to date; we construct ukm, an upto-date knowledge base of the film industry; we propose mvsd, a state-of-the-art spoiler detection system that takes external knowledge and user interactions into account. extensive experiments demonstrate that mvsd achieves state-of-the-art performance on two datasets while showcasing the benefits of incorporating movie knowledge and user behavior in spoiler detection. we leave it for future work to further check the labels in the lcs dataset.