mlsanityData to 14 Oct 2026

Unveiling the Hidden: Movie Genre and User Bias in Spoiler Detection

arXiv 2504.17834 ↗First posted 24 Apr 2025cs.IR cs.CLcited by 0

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Abstract (arXiv)

Spoilers in movie reviews are important on platforms like IMDb and Rotten Tomatoes, offering benefits and drawbacks. They can guide some viewers' choices but also affect those who prefer no plot details in advance, making effective spoiler detection essential. Existing spoiler detection methods mainly analyze review text, often overlooking the impact of movie genres and user bias, limiting their effectiveness. To address this, we analyze movie review data, finding genre-specific variations in spoiler rates and identifying that certain users are more likely to post spoilers. Based on these findings, we introduce a new spoiler detection framework called GUSD (The code is available at https://github.com/AI-explorer-123/GUSD) (Genre-aware and User-specific Spoiler Detection), which incorporates genre-specific data and user behavior bias. User bias is calculated through dynamic graph modeling of review history. Additionally, the R2GFormer module combines RetGAT (Retentive Graph Attention Network) for graph information and GenreFormer for genre-specific aggregation. The GMoE (Genre-Aware Mixture of Experts) model further assigns reviews to specialized experts based on genre. Extensive testing on benchmark datasets shows that GUSD achieves state-of-the-art results. This approach advances spoiler detection by addressing genre and user-specific patterns, enhancing user experience on movie review platforms.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 45 references that are in the map. First posted on arXiv 24 Apr 2025.

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30 of 45 references.

References · 30

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  5. 2017Graph Attention Networkscited by 8,923
  6. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  7. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  8. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,073
  9. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  10. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  11. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,254
  12. 2022Recipe for a General, Powerful, Scalable Graph Transformercited by 713
18 more
  1. 2023Retentive Network: A Successor to Transformer for Large Language Modelscited by 613
  2. 2020Heterogeneous Graph Transformercited by 582
  3. 2015Diffusion-Convolutional Neural Networkscited by 268
  4. 2023From Sparse to Soft Mixtures of Expertscited by 248
  5. 2021Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networkscited by 176
  6. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 157
  7. 2019Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferencescited by 87
  8. 2019Fine-Grained Spoiler Detection from Large-Scale Review Corporacited by 77
  9. 2023RMT: Retentive Networks Meet Vision Transformerscited by 68
  10. 2019k-hop Graph Neural Networkscited by 57
  11. 2020Multi-hop Attention Graph Neural Networkcited by 53
  12. 2021Think about it! Improving defeasible reasoning by first modeling the question scenariocited by 20
  13. 2023BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Expertscited by 18
  14. 2023LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detectioncited by 17
  15. 2021"Killing Me" Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanismcited by 5
  16. 2023Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networkscited by 4
  17. 2021Spoiler Alert: Using Natural Language Processing to Detect Spoilers in Book Reviewscited by 1
  18. 2021Spoiler in a Textstack: How Much Can Transformers Help?cited by 1

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