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
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.