Trigger Warnings: Bootstrapping a Violence Detector for FanFiction

Magdalena Wolska; Christopher Schröder; Ole Borchardt; Benno Stein; Martin Potthast

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arXiv · 2209.04409

Trigger Warnings: Bootstrapping a Violence Detector for FanFiction

Magdalena Wolska·Christopher Schröder·Ole Borchardt·Benno Stein·Martin Potthast

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Abstract

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.

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Magdalena Wolska, Christopher Schröder, Ole Borchardt, Benno Stein, Martin Potthast. 2022-09-09. Trigger Warnings: Bootstrapping a Violence Detector for FanFiction. https://arxiv.org/abs/2209.04409

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