  Online movie review websites are valuable for information and discussion
about movies. However, the massive spoiler reviews detract from the
movie-watching experience, making spoiler detection an important task. Previous
methods simply focus on reviews' text content, ignoring the heterogeneity of
information in the platform. For instance, the metadata and the corresponding
user's information of a review could be helpful. Besides, the spoiler language
of movie reviews tends to be genre-specific, thus posing a domain
generalization challenge for existing methods. To this end, we propose MMoE, a
multi-modal network that utilizes information from multiple modalities to
facilitate robust spoiler detection and adopts Mixture-of-Experts to enhance
domain generalization. MMoE first extracts graph, text, and meta feature from
the user-movie network, the review's textual content, and the review's metadata
respectively. To handle genre-specific spoilers, we then adopt
Mixture-of-Experts architecture to process information in three modalities to
promote robustness. Finally, we use an expert fusion layer to integrate the
features from different perspectives and make predictions based on the fused
embedding. Experiments demonstrate that MMoE achieves state-of-the-art
performance on two widely-used spoiler detection datasets, surpassing previous
SOTA methods by 2.56% and 8.41% in terms of accuracy and F1-score. Further
experiments also demonstrate MMoE's superiority in robustness and
generalization.
