Comparing Different Supervised Approaches to Hate Speech Detection

Description

This paper reports on the systems the InriaFBK Team submitted to the EVALITA 2018-Shared Task on Hate Speech Detection in Italian Twitter and Facebook posts (HaSpeeDe). Our submissions were based on three separate classes of models: a model using a recurrent layer, an ngram-based neural network and a LinearSVC. For the Facebook task and the two cross-domain tasks we used the recurrent model and obtained promising results, especially in the cross-domain setting. For Twitter, we used an ngram-based neural network and the LinearSVC-based model.

Abstract (Italian)

Questo articolo descrive i modelli del team InriaFBK per lo Shared Task on Hate Speech Detection in Italian Twitter and Facebook posts (HaSpeeDe) di EVALITA 2018. Tre classi di modelli differenti sono state utilizzate: un model-lo che usa un livello ricorrente, una rete neurale basata su ngrammi e un modello basato su LinearSVC. Per Facebook e i due task cross-domain, sì e scelto un modello ricorrente che ha ottenuto buoni risultati, specialmente per quanto riguarda i task cross-domain. Per Twitter, sono stati utilizzati la rete neurale basata su ngrammi e il modello basato su LinearSVC.

Abstract

International audience

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