Name: ArMI: Arabic Misogynistic Dataset
Description: Arabic multidialectal dataset for misogynistic language
Provider: The ORSAM Center for Middle Eastern Studies, University of Alberta
Paper Title: ArMI at FIRE 2021: Overview of the First Shared Task on Arabic Misogyny Identification
Tasks: misogyny identification
Venue Name: Forum for Information Retrieval Evaluation
Authors: Hala Mulki, Bilal Ghanem
Abstract: This paper provides an overview of the organization, results and main findings of the first
  shared task on misogyny identification in Arabic tweets. Arabic Misogyny Identification task
  (ArMI) is introduced within the Hate Speech and Offensive Content detection (HASOC)
  track at FIRE-2021. The ArMI task combines two related classification subtasks: a main
  binary classification subtask for detecting the presence of misogynistic language, and a finegrained multi-class classification subtask for identifying seven misogynistic behaviors found in misogynistic contents. The data provided for this task is a Twitter dataset composed of 9,833 tweets written in modern standard Arabic (MSA) and several Arabic dialects including Levantine, Egyptian and Gulf. ArMI at FIRE-2021 has got a total of 15 submitted runs for Sub-task A and 13 runs for Sub-task B provided by six different teams. The systems introduced by the participants employed various methods including feature-based, neural networks using either classical machine learning techniques, ensemble methods or transformers. The best performing system achieved an F-measure of 91.4% and 66.5% for subtask A and subtask B, respectively. This indicates that misogynistic language detection and misogynistic behaviors identification in Arabic textual contents can be, effectively, addressed using transformer-based approaches.