Even though hate speech (HS) online has been an important object of research in the last decade, most HS-related corpora over-simplify the phenomenon of hate by attempting to label user comments as "hate" or "neutral". This ignores the complex and subjective nature of HS, which limits the real-life applicability of classifiers trained on these corpora. In this study, we present the M-Phasis corpus, a corpus of ~9k German and French user comments collected from migration-related news articles. It goes beyond the "hate"-"neutral" dichotomy and is instead annotated with 23 features, which in combination become descriptors of various types of speech, ranging from critical comments to implicit and explicit expressions of hate. The annotations are performed by 4 native speakers per language and achieve high (0.77 <= k <= 1) inter-annotator agreements. Besides describing the corpus creation and presenting insights from a content, error and domain analysis, we explore its data characteristics by training several classification baselines.
翻译：尽管网上仇恨言论(HS)是过去十年研究的一个重要对象,但大多数与HS有关的公司通过试图将用户的评论贴上“仇恨”或“中立”的标签,过度简化了仇恨现象。这忽略了HS的复杂和主观性质,它限制了接受过这些公司培训的分类人员的实际适用性。在本研究报告中,我们介绍了M-Phasis 文集,这是从与移民有关的新闻文章中收集的一套~9k的德国和法国用户评论。它超越了“仇恨”中立的二分法,而是附加了23个特征,这些特征成为各种言论的描述,从批评性评论到隐含和明示的仇恨表达。说明由每个语言的4名当地演讲人进行,达成很高的(0.77 ⁇ k ⁇ ⁇ 1 ) 间协议。除了描述该文的创建和从内容、错误和领域分析中提供深刻的见解外,我们还通过培训若干分类基准来探讨其数据特征。
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