@InProceedings{zampieri-EtAl:2019:S19-2,
  author    = {Zampieri, Marcos  and  Malmasi, Shervin  and  Nakov, Preslav  and  Rosenthal, Sara  and  Farra, Noura  and  Kumar, Ritesh},
  title     = {SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {75--86},
  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, and it featured three sub-tasks. In sub-task A, systems were asked to discriminate between offensive and non-offensive posts. In sub-task B, systems had to identify 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, nearly 800 teams signed up to participate in the task and 115 of them submitted results, which are presented and analyzed in this report.},
  url       = {http://www.aclweb.org/anthology/S19-2010}
}