Misogyny Detection and Classification in English Tweets: The Experience of the ITT Team
Elena Shushkevich, John Cardiff · Accademia University Press eBooks · 2018
The problem of online misogyny and women-based offending has become increasingly widespread, and the automatic detection of such messages is an urgent priority. In this paper, we present an approach based on an ensemble of Logistic Regression, Support Vector Machines, and Naïve Bayes models for the detection of misogyny in texts extracted from the Twitter platform. Our method has been presented in the framework of the participation in the Automatic Misogyny Identification (AMI) Shared Task in the EVALITA 2018 evaluation campaign.