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Even though hate speech (HS) online has been an important object of researchin the last decade, most HS-related corpora over-simplify the phenomenon ofhate by attempting to label user comments as "hate" or "neutral". This ignoresthe complex and subjective nature of HS, which limits the real-lifeapplicability of classifiers trained on these corpora. In this study, wepresent the M-Phasis corpus, a corpus of ~9k German and French user commentscollected from migration-related news articles. It goes beyond the"hate"-"neutral" dichotomy and is instead annotated with 23 features, which incombination become descriptors of various types of speech, ranging fromcritical comments to implicit and explicit expressions of hate. The annotationsare performed by 4 native speakers per language and achieve high (0.77 <= k <=1) inter-annotator agreements. Besides describing the corpus creation andpresenting insights from a content, error and domain analysis, we explore itsdata characteristics by training several classification baselines.

Placing M-Phasis on the Plurality of Hate: A Feature-Based Corpus of Hate Online