  Hate speech represents a pervasive and detrimental form of online discourse,
often manifested through an array of slurs, from hateful tweets to defamatory
posts. As such speech proliferates, it connects people globally and poses
significant social, psychological, and occasionally physical threats to
targeted individuals and communities. Current computational linguistic
approaches for tackling this phenomenon rely on labelled social media datasets
for training. For unifying efforts, our study advances in the critical need for
a comprehensive meta-collection, advocating for an extensive dataset to help
counteract this problem effectively. We scrutinized over 60 datasets,
selectively integrating those pertinent into MetaHate. This paper offers a
detailed examination of existing collections, highlighting their strengths and
limitations. Our findings contribute to a deeper understanding of the existing
datasets, paving the way for training more robust and adaptable models. These
enhanced models are essential for effectively combating the dynamic and complex
nature of hate speech in the digital realm.
