{"url":"/dataset/tun-el","name":"TUN-EL","full_name":null,"description_markdown":"Arabic multi-dialectal hate speech dataset. It consists of 23,033 tweets labelled for Hate, Abusive and Normal in three Arabic dialects Egyptian, Lebaneese and Tunisian.\r\n\r\nHow to cite this work: Badri, Nabil, Ferihane Kboubi, and Anja Habacha Chaibi. \"Towards automatic detection of inappropriate content in multi-dialectic Arabic text.\" International Conference on Computational Collective Intelligence. Cham: Springer International Publishing, 2022.","description_withheld":null,"homepage":"https://github.com/NabilBADRI/Towards-Automatic-Detection-of-Inappropriate-Content-in-Multi-dialectic-Arabic-Text","introduced_date":"2025-03-04","introduced_date_note":null,"introduced_by":null,"license":{"name":"CC BY (Citation Required)","url":null},"modalities":[],"tasks":[{"name":"Hate Speech Detection","url":"/task/hate-speech-detection","datasets_with_task":"/datasets/task/hate-speech-detection"},{"name":"Hate Span Identification","url":"/task/hate-span-identification","datasets_with_task":"/datasets/task/hate-span-identification"}],"languages":[{"name":"Arabic","url":"/datasets/language/arabic"}],"variants":["TUN-EL"],"data_loaders":[{"repo":"https://github.com/NabilBADRI/Towards-Automatic-Detection-of-Inappropriate-Content-in-Multi-dialectic-Arabic-Text","url":"https://github.com/NabilBADRI/Towards-Automatic-Detection-of-Inappropriate-Content-in-Multi-dialectic-Arabic-Text","frameworks":[]}],"num_papers_in_archive":0,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}