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

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