  The increased proliferation of abusive content on social media platforms has
a negative impact on online users. The dread, dislike, discomfort, or mistrust
of lesbian, gay, transgender or bisexual persons is defined as
homophobia/transphobia. Homophobic/transphobic speech is a type of offensive
language that may be summarized as hate speech directed toward LGBT+ people,
and it has been a growing concern in recent years. Online
homophobia/transphobia is a severe societal problem that can make online
platforms poisonous and unwelcome to LGBT+ people while also attempting to
eliminate equality, diversity, and inclusion. We provide a new hierarchical
taxonomy for online homophobia and transphobia, as well as an expert-labelled
dataset that will allow homophobic/transphobic content to be automatically
identified. We educated annotators and supplied them with comprehensive
annotation rules because this is a sensitive issue, and we previously
discovered that untrained crowdsourcing annotators struggle with diagnosing
homophobia due to cultural and other prejudices. The dataset comprises 15,141
annotated multilingual comments. This paper describes the process of building
the dataset, qualitative analysis of data, and inter-annotator agreement. In
addition, we create baseline models for the dataset. To the best of our
knowledge, our dataset is the first such dataset created. Warning: This paper
contains explicit statements of homophobia, transphobia, stereotypes which may
be distressing to some readers.
