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        <title>Trigger Warnings: Bootstrapping a Violence Detector for Fan Fiction</title>
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        <namePart type="given">Magdalena</namePart>
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    <abstract>We present the first dataset and evaluation results on a newly defined task: assigning trigger warnings. We introduce a labeled corpus of narrative fiction from Archive of Our Own (AO3), a popular fan fiction site, and define a document-level classification task to determine whether or not to assign a trigger warning to an English story. We focus on the most commonly assigned trigger type “violence’ using the warning labels provided by AO3 authors as ground-truth labels. We trained SVM, BERT, and Longfomer models on three datasets sampled from the corpus and achieve F1 scores between 0.8 and 0.9, indicating that assigning trigger warnings for violence is feasible.</abstract>
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%0 Conference Proceedings
%T Trigger Warnings: Bootstrapping a Violence Detector for Fan Fiction
%A Wolska, Magdalena
%A Wiegmann, Matti
%A Schröder, Christopher
%A Borchardt, Ole
%A Stein, Benno
%A Potthast, Martin
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wolska-etal-2023-trigger
%X We present the first dataset and evaluation results on a newly defined task: assigning trigger warnings. We introduce a labeled corpus of narrative fiction from Archive of Our Own (AO3), a popular fan fiction site, and define a document-level classification task to determine whether or not to assign a trigger warning to an English story. We focus on the most commonly assigned trigger type “violence’ using the warning labels provided by AO3 authors as ground-truth labels. We trained SVM, BERT, and Longfomer models on three datasets sampled from the corpus and achieve F1 scores between 0.8 and 0.9, indicating that assigning trigger warnings for violence is feasible.
%R 10.18653/v1/2023.findings-emnlp.41
%U https://aclanthology.org/2023.findings-emnlp.41
%U https://doi.org/10.18653/v1/2023.findings-emnlp.41
%P 569-576

Markdown (Informal)

[Trigger Warnings: Bootstrapping a Violence Detector for Fan Fiction](https://aclanthology.org/2023.findings-emnlp.41) (Wolska et al., Findings 2023)

• Trigger Warnings: Bootstrapping a Violence Detector for Fan Fiction (Wolska et al., Findings 2023)

ACL

• Magdalena Wolska, Matti Wiegmann, Christopher Schröder, Ole Borchardt, Benno Stein, and Martin Potthast. 2023. Trigger Warnings: Bootstrapping a Violence Detector for Fan Fiction. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 569–576, Singapore. Association for Computational Linguistics.