TITLE: Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language 

ABSTRACT: Online misogyny has become an increasing worry for Arab women who experience
gender-based online abuse on a daily basis. Misogyny automatic detection
systems can assist in the prohibition of anti-women Arabic toxic content.
Developing such systems is hindered by the lack of the Arabic misogyny
benchmark datasets. In this paper, we introduce an Arabic Levantine Twitter
dataset for Misogynistic language (LeT-Mi) to be the first benchmark dataset
for Arabic misogyny. We further provide a detailed review of the dataset
creation and annotation phases. The consistency of the annotations for the
proposed dataset was emphasized through inter-rater agreement evaluation
measures. Moreover, Let-Mi was used as an evaluation dataset through
binary/multi-/target classification tasks conducted by several state-of-the-art
machine learning systems along with Multi-Task Learning (MTL) configuration.
The obtained results indicated that the performances achieved by the used
systems are consistent with state-of-the-art results for languages other than
Arabic, while employing MTL improved the performance of the misogyny/target
classification tasks.
Online misogyny has become an increasing worry for Arab women who experience
gender-based online abuse on a daily basis. Misogyny automatic detection
systems can assist in the prohibition of anti-women Arabic toxic content.
Developing such systems is hindered by the lack of the Arabic misogyny
benchmark datasets. In this paper, we introduce an Arabic Levantine Twitter
dataset for Misogynistic language (LeT-Mi) to be the first benchmark dataset
for Arabic misogyny. We further provide a detailed review of the dataset
creation and annotation phases. The consistency of the annotations for the
proposed dataset was emphasized through inter-rater agreement evaluation
measures. Moreover, Let-Mi was used as an evaluation dataset through
binary/multi-/target classification tasks conducted by several state-of-the-art
machine learning systems along with Multi-Task Learning (MTL) configuration.
The obtained results indicated that the performances achieved by the used
systems are consistent with state-of-the-art results for languages other than
Arabic, while employing MTL improved the performance of the misogyny/target
classification tasks.
Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language
http://arxiv.org/abs/2103.10195v1