# TWeddit : A Dataset of Triggering Stories Predominantly Shared by Women on Reddit

arXiv: https://arxiv.org/abs/2601.11819 · HTML: https://mlsanity.com/paper/2601.11819/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Builds on (most cited first, 7 of 7)

- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [#RoeOverturned: Twitter Dataset on the Abortion Rights Controversy](https://mlsanity.com/paper/2302.01439/) (2023)
- [A Data Fusion Framework for Multi-Domain Morality Learning](https://mlsanity.com/paper/2304.02144/) (2023)
- [Towards Automated Sexual Violence Report Tracking](https://mlsanity.com/paper/1911.06961/) (2019)
- [Linguistic Characterization of Divisive Topics Online: Case Studies on Contentiousness in Abortion, Climate Change, and Gun Control](https://mlsanity.com/paper/2108.13556/) (2021)
- [SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories](https://mlsanity.com/paper/1809.04739/) (2018)
- [Polarization and Morality: Lexical Analysis of Abortion Discourse on Reddit](https://mlsanity.com/paper/2407.00455/) (2024)

## Abstract (arXiv)

Warning: This paper may contain examples and topics that may be disturbing to some readers, especially survivors of miscarriage and sexual violence. People affected by abortion, miscarriage, or sexual violence often share their experiences on social media to express emotions and seek support. On public platforms like Reddit, where users can post long, detailed narratives (up to 40,000 characters), readers may be exposed to distressing content. Although Reddit allows manual trigger warnings, many users omit them due to limited awareness or uncertainty about which categories apply. There is scarcity of datasets on Reddit stories labeled for triggering experiences. We propose a curated Reddit dataset, TWeddit, covering triggering experiences related to issues majorly faced by women. Our linguistic analyses show that annotated stories in TWeddit express distinct topics and moral foundations, making the dataset useful for a wide range of future research.