Trigger Warnings: Bootstrapping a Violence Detector for FanFiction

09/09/2022

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by   Magdalena Wolska, et al.

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We present the first dataset and evaluation results on a newly defined computational task of trigger warning assignment. Labeled corpus data has been compiled from narrative works hosted on Archive of Our Own (AO3), a well-known fanfiction site. In this paper, we focus on the most frequently assigned trigger type–violence–and define a document-level binary classification task of whether or not to assign a violence trigger warning to a fanfiction, exploiting warning labels provided by AO3 authors. SVM and BERT models trained in four evaluation setups on the corpora we compiled yield F_1 results ranging from 0.585 to 0.798, proving the violence trigger warning assignment to be a doable, however, non-trivial task.