Automatic Misogyny Detection in Social Media: A Survey

Abstract

This article presents?a?survey of?automated?misogyny identification?techniques?in social media, especially in Twitter. This problem is urgent?because of?the?high speed?at which messages on?social platforms grow and?the?widespread?use of?offensive language (including misogynistic?language) in them. In this?article?we survey?approaches proposed in the literature to solve the problem of misogynistic message?recognition. These include?classical machine learning models like Sup-port Vector Machine, Naive Bayes, Logistic Regression and?ensembles of different classical machine learning models and deep neural networks?such as?Long Short-term memory and Convolutional Neural Networks.?We consider?results of experiments with these?models in different languages: English, Spanish and Italian tweets. The survey describes some features?which help to identify misogynistic tweets and some challenges which aim was to create misogyny language classifiers.?The survey includes?not?only?models?which help to identify misogyny language, but also systems which help?to recognize a target of an offense (an individual or a group of persons).