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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.
Cardiff, J. & Shushkevich, E. (2018). Misogyny detection and classification in english tweets:the experience of the ITT team. In Proceedings of the Sixth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2018) co-located with the Fifth hItalian Conference on Computational Linguistics (CLiC-it 2018) Turin, Italy, December 12-13.