# If there's a Trigger Warning, then where's the Trigger? Investigating Trigger Warnings at the Passage Level

arXiv: https://arxiv.org/abs/2404.09615 · HTML: https://mlsanity.com/paper/2404.09615/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://mlsanity.com/paper/2110.05719/) (2021)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [Offensive Language Identification in Greek](https://mlsanity.com/paper/2003.07459/) (2020)
- [Handling and Presenting Harmful Text in NLP Research](https://mlsanity.com/paper/2204.14256/) (2022)
- [Trigger Warnings: Bootstrapping a Violence Detector for FanFiction](https://mlsanity.com/paper/2209.04409/) (2022)

## Built on by (most cited first, 1 of 1)

- [MIL-BERT: Classification of Arbitrarily Large Text with Performance and Explanatory Guarantees](https://mlsanity.com/paper/2608.20636/) (2026)

## Abstract (arXiv)

Trigger warnings are labels that preface documents with sensitive content if this content could be perceived as harmful by certain groups of readers. Since warnings about a document intuitively need to be shown before reading it, authors usually assign trigger warnings at the document level. What parts of their writing prompted them to assign a warning, however, remains unclear. We investigate for the first time the feasibility of identifying the triggering passages of a document, both manually and computationally. We create a dataset of 4,135 English passages, each annotated with one of eight common trigger warnings. In a large-scale evaluation, we then systematically evaluate the effectiveness of fine-tuned and few-shot classifiers, and their generalizability. We find that trigger annotation belongs to the group of subjective annotation tasks in NLP, and that automatic trigger classification remains challenging but feasible.