# The Uli Dataset: An Exercise in Experience Led Annotation of oGBV

arXiv: https://arxiv.org/abs/2311.09086 · HTML: https://mlsanity.com/paper/2311.09086/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [On Releasing Annotator-Level Labels and Information in Datasets](https://mlsanity.com/paper/2110.05699/) (2021)
- [Understanding Abuse: A Typology of Abusive Language Detection Subtasks](https://mlsanity.com/paper/1705.09899/) (2017)
- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text](https://mlsanity.com/paper/2006.00206/) (2020)
- [SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification](https://mlsanity.com/paper/2004.14454/) (2020)
- [Aggression-annotated Corpus of Hindi-English Code-mixed Data](https://mlsanity.com/paper/1803.09402/) (2018)
- [Hate Speech detection in the Bengali language: A dataset and its baseline evaluation](https://mlsanity.com/paper/2012.09686/) (2020)
- [Developing a Multilingual Annotated Corpus of Misogyny and Aggression](https://mlsanity.com/paper/2003.07428/) (2020)
- [Hostility Detection Dataset in Hindi](https://mlsanity.com/paper/2011.03588/) (2020)
- [Hate and Offensive Speech Detection in Hindi and Marathi](https://mlsanity.com/paper/2110.12200/) (2021)
- [Modeling Trolling in Social Media Conversations](https://mlsanity.com/paper/1612.05310/) (2016)
- [Database of Indian Social Media Influencers on Twitter](https://mlsanity.com/paper/2203.09193/) (2022)

## Built on by (most cited first, 3 of 3)

- [Overview of the 2023 ICON Shared Task on Gendered Abuse Detection in Indic Languages](https://mlsanity.com/paper/2401.03677/) (2024)
- [Silencing Empowerment, Allowing Bigotry: Auditing the Moderation of Hate Speech on Twitch](https://mlsanity.com/paper/2506.07667/) (2025)
- [GENDIS: Gender-Related Discourse and Stance in Multilingual YouTube Comments from India, Kenya, and Nigeria](https://mlsanity.com/paper/2610.17778/) (2026)

## Abstract (arXiv)

Online gender based violence has grown concomitantly with adoption of the internet and social media. Its effects are worse in the Global majority where many users use social media in languages other than English. The scale and volume of conversations on the internet has necessitated the need for automated detection of hate speech, and more specifically gendered abuse. There is, however, a lack of language specific and contextual data to build such automated tools. In this paper we present a dataset on gendered abuse in three languages- Hindi, Tamil and Indian English. The dataset comprises of tweets annotated along three questions pertaining to the experience of gender abuse, by experts who identify as women or a member of the LGBTQIA community in South Asia. Through this dataset we demonstrate a participatory approach to creating datasets that drive AI systems.