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.

在线性别暴力与互联网和社交媒体的采用同时增长。它在全球多数国家尤其严重，因为许多用户使用的社交媒体语言不是英语。由于互联网上对话的规模和数量，需要自动检测仇恨言论，尤其是性别虐待。然而，目前缺乏特定语言和语境数据来构建这样的自动化工具。本文介绍了一个关于三种语言 (印地语、泰米尔语和印度英语) 中性别虐待的数据集。该数据集包括在南亚的自称是妇女或 LGBTQIA 群体成员的专家标注的推文，涉及到性别虐待的经历的三个问题。通过这个数据集，我们展示了一种参与性的方法来创建推动 AI 系统的数据集。