The Uli Dataset: An Exercise in Experience Led Annotation of oGBV
Arnav Arora, Maha Jinadoss, Cheshta Arora, Denny George, Brindaalakshmi, Haseena Dawood Khan, Kirti Rawat, Div, Ritash, Seema Mathur, Shivani Yadav, Shehla Rashid Shora, Rie Raut, Sumit Pawar, Apurva Paithane, Sonia, Vivek, Dharini Priscilla, Khairunnisha, Grace Banu, Ambika Tandon, Rishav Thakker, Rahul Dev Korra, Aatman Vaidya, Tarunima Prabhakar
TL;DR
oGBV is pervasive in the Global majority and underrepresented in language-specific datasets. This paper addresses this gap by constructing a survivor-centered multilingual dataset in Hindi, Tamil, and Indian English using a two-stage corpus creation process with semi-supervised stratified pooling and expert annotation. It releases annotator-level labels under CC BY 4.0, analyzes cross-language agreement, and provides baseline model evaluations with IndicBERT and XLM-T, highlighting language-specific challenges. The work advances inclusive, context-aware oGBV detection and informs participatory AI practices with implications for future multimodal extensions.
Abstract
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.