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The increased proliferation of abusive content on social media platforms hasa negative impact on online users. The dread, dislike, discomfort, or mistrustof lesbian, gay, transgender or bisexual persons is defined ashomophobia/transphobia. Homophobic/transphobic speech is a type of offensivelanguage that may be summarized as hate speech directed toward LGBT+ people,and it has been a growing concern in recent years. Onlinehomophobia/transphobia is a severe societal problem that can make onlineplatforms poisonous and unwelcome to LGBT+ people while also attempting toeliminate equality, diversity, and inclusion. We provide a new hierarchicaltaxonomy for online homophobia and transphobia, as well as an expert-labelleddataset that will allow homophobic/transphobic content to be automaticallyidentified. We educated annotators and supplied them with comprehensiveannotation rules because this is a sensitive issue, and we previouslydiscovered that untrained crowdsourcing annotators struggle with diagnosinghomophobia due to cultural and other prejudices. The dataset comprises 15,141annotated multilingual comments. This paper describes the process of buildingthe dataset, qualitative analysis of data, and inter-annotator agreement. Inaddition, we create baseline models for the dataset. To the best of ourknowledge, our dataset is the first such dataset created. Warning: This papercontains explicit statements of homophobia, transphobia, stereotypes which maybe distressing to some readers.

Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube Comments