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        <title>Fine-Grained Spoiler Detection from Large-Scale Review Corpora</title>
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        <namePart type="given">Mengting</namePart>
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    <name type="personal">
        <namePart type="given">Rishabh</namePart>
        <namePart type="family">Misra</namePart>
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    <name type="personal">
        <namePart type="given">Julian</namePart>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1248</identifier>
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%0 Conference Proceedings
%T Fine-Grained Spoiler Detection from Large-Scale Review Corpora
%A Wan, Mengting
%A Misra, Rishabh
%A Nakashole, Ndapa
%A McAuley, Julian
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F wan-etal-2019-fine
%X 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.
%R 10.18653/v1/P19-1248
%U https://aclanthology.org/P19-1248
%U https://doi.org/10.18653/v1/P19-1248
%P 2605-2610

Markdown (Informal)

[Fine-Grained Spoiler Detection from Large-Scale Review Corpora](https://aclanthology.org/P19-1248) (Wan et al., ACL 2019)

• Fine-Grained Spoiler Detection from Large-Scale Review Corpora (Wan et al., ACL 2019)

ACL

• Mengting Wan, Rishabh Misra, Ndapa Nakashole, and Julian McAuley. 2019. Fine-Grained Spoiler Detection from Large-Scale Review Corpora. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2605–2610, Florence, Italy. Association for Computational Linguistics.