Working Notes of the Workshop Arabic Misogyny Identification (ArMI-2021)
Hala Mulki, Bilal Ghanem · Forum for Information Retrieval Evaluation · 2021
This paper provides an overview 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 fine-grained multi-class classification subtask for identifying seven misogynistic behaviors found in misogynistic contents of 9,833 Arabic/dialectal tweets1. 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.