datahub/examples/turing/content/datasets/measuring-hate-speech.md

title, link-to-publication, link-to-data, task-description, details-of-task, size-of-dataset, percentage-abusive, language, level-of-annotation, platform, medium, reference

title	link-to-publication	link-to-data	task-description	details-of-task	size-of-dataset	percentage-abusive	language	level-of-annotation	platform	medium	reference
Measuring Hate Speech	https://arxiv.org/abs/2009.10277	https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech	10 ordinal labels (sentiment, (dis)respect, insult, humiliation, inferior status, violence, dehumanization, genocide, attack/defense, hate speech), which are debiased and aggregated into a continuous hate speech severity score (hate_speech_score) that includes a region for counterspeech & supportive speeech. Includes 8 target identity groups (race/ethnicity, religion, national origin/citizenship, gender, sexual orientation, age, disability, political ideology) and 42 identity subgroups.	Hate speech measurement on social media in English	39,565 comments annotated by 7,912 annotators on 10 ordinal labels, for 1,355,560 total labels.	25	English	Social media comment	Twitter Reddit Youtube	Text	Kennedy, C. J., Bacon, G., Sahn, A., & von Vacano, C. (2020). Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application. arXiv preprint arXiv:2009.10277.