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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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Contributor : Michele Corazza <>
Submitted on : Tuesday, November 13, 2018 - 10:17:48 AM
Last modification on : Monday, October 12, 2020 - 10:30:35 AM
Long-term archiving on: : Thursday, February 14, 2019 - 12:51:20 PM


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  • HAL Id : hal-01920266, version 1



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. ⟨hal-01920266⟩



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