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.

恶意言论是一种广泛而有害的在线话语形式，通常通过一系列诋毁性的言辞来表现，如令人厌恶的推文和诽谤帖子。本研究旨在解决这一现象，提出了一个全面的元数据集，并对现有的收集进行了详细的研究，以推动更强大和适应性更强的模型的训练，以有效对抗数字领域中恶意言论的动态和复杂性。