datahub/examples/turing/content/datasets/abusive-eval-v1-0.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
AbuseEval v1.0	http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.760.pdf	https://github.com/tommasoc80/AbuseEval	Explicitness annotation of offensive and abusive content	Enriched versions of the OffensEval/OLID dataset with the distinction of explicit/implicit offensive messages and the new dimension for abusive messages. Labels for offensive language: EXPLICIT, IMPLICT, NOT; Labels for abusive language: EXPLICIT, IMPLICT, NOTABU	14100	20.75	English	Tweets	Twitter	Text	Caselli, T., Basile, V., Jelena, M., Inga, K., and Michael, G. 2020. "I feel offended, don’t be abusive! implicit/explicit messages in offensive and abusive language". The 12th Language Resources and Evaluation Conference (pp. 6193-6202). European Language Resources Association.