Comparing Different Supervised Approaches to Hate Speech Detection

Michele Corazza 1 Stefano Menini 2 Pinar Arslan 1 Rachele Sprugnoli 2 Elena Cabrio 1 Sara Tonelli 2 Serena Villata 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : 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.
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Communication dans un congrès
EVALITA 2018, Dec 2018, Turin, Italy. Proceedings ov EVALITA 2018
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https://hal.archives-ouvertes.fr/hal-01920266
Contributeur : Michele Corazza <>
Soumis le : mardi 13 novembre 2018 - 10:17:48
Dernière modification le : mercredi 14 novembre 2018 - 01:17:38

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Michele Corazza, Stefano Menini, Pinar Arslan, Rachele Sprugnoli, Elena Cabrio, et al.. Comparing Different Supervised Approaches to Hate Speech Detection. EVALITA 2018, Dec 2018, Turin, Italy. Proceedings ov EVALITA 2018. 〈hal-01920266〉

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