mlsanityData to 14 Oct 2026

Fine-Grained Spoiler Detection from Large-Scale Review Corpora

arXiv 1905.13416 ↗First posted 31 May 2019cs.CLcited by 77

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Abstract (arXiv)

This paper presents computational approaches for automatically detecting critical plot twists in reviews of media products. First, we created a large-scale book review dataset that includes fine-grained spoiler annotations at the sentence-level, as well as book and (anonymized) user information. Second, we carefully analyzed this dataset, and found that: spoiler language tends to be book-specific; spoiler distributions vary greatly across books and review authors; and spoiler sentences tend to jointly appear in the latter part of reviews. Third, inspired by these findings, we developed an end-to-end neural network architecture to detect spoiler sentences in review corpora. Quantitative and qualitative results demonstrate that the proposed method substantially outperforms existing baselines.

Key facts. Cited by 77 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 May 2019.

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Citing papers per year: 2021: 7; 2022: 10; 2023: 14; 2024: 10; 2025: 18; 2026: 18.

77 papers.

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38 more
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  16. 2021"Killing Me" Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanismcited by 5
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  34. 2021Spoiler Alert: Using Natural Language Processing to Detect Spoilers in Book Reviewscited by 1
  35. 2021Graph Convolutional Embeddings for Recommender Systemscited by 1
  36. 2021Modeling Social Readers: Novel Tools for Addressing Reception from Online Book Reviewscited by 1
  37. 2021Spoiler in a Textstack: How Much Can Transformers Help?cited by 1
  38. 2022ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervisioncited by 1

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