Online hate speech is a newborn problem in our modern society which is growing at a steady rate exploiting weaknesses of the corresponding regimes that characterise several social media platforms. Therefore, this phenomenon is mainly cultivated through such comments, either during users' interaction or on posted multimedia context. Nowadays, giant companies own platforms where many millions of users log in daily. Thus, protection of their users from exposure to similar phenomena for keeping up with the corresponding law, as well as for retaining a high quality of offered services, seems mandatory. Having a robust and reliable mechanism for identifying and preventing the uploading of related material would have a huge effect on our society regarding several aspects of our daily life. On the other hand, its absence would deteriorate heavily the total user experience, while its erroneous operation might raise several ethical issues. In this work, we present a protocol for creating a more suitable dataset, regarding its both informativeness and representativeness aspects, favouring the safer capture of hate speech occurrence, without at the same time restricting its applicability to other classification problems. Moreover, we produce and publish a textual dataset with two variants: binary and multi-label, called `ETHOS', based on YouTube and Reddit comments validated through figure-eight crowdsourcing platform. Our assumption about the production of more compatible datasets is further investigated by applying various classification models and recording their behaviour over several appropriate metrics.

在本文中，我们提出了基于YouTube和Reddit评论的文本数据集'EHTOS'，并介绍了用于创建该数据集的注释协议，旨在平衡数据。我们的关键假设是，即使从这样耗时的过程中获得一小部分标记数据，我们也可以保证在所检查的材料中发现仇恨言论。

ETHOS：一个在线仇恨言论检测数据集