SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)
Abstract
We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains over 14,000 English tweets. It featured three sub-tasks. In sub-task A, the goal was to discriminate between offensive and non-offensive posts. In sub-task B, the focus was on the type of offensive content in the post. Finally, in sub-task C, systems had to detect the target of the offensive posts. OffensEval attracted a large number of participants and it was one of the most popular tasks in SemEval-2019. In total, about 800 teams signed up to participate in the task, and 115 of them submitted results, which we present and analyze in this report.
Keywords
Cite
@article{arxiv.1903.08983,
title = {SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)},
author = {Marcos Zampieri and Shervin Malmasi and Preslav Nakov and Sara Rosenthal and Noura Farra and Ritesh Kumar},
journal= {arXiv preprint arXiv:1903.08983},
year = {2019}
}
Comments
Proceedings of the International Workshop on Semantic Evaluation (SemEval)