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
University of Minnesota M.S. thesis. May 2020. Major: Computer Science. Advisor: Richard Maclin. 1 c...
The pervasiveness of the Internet and social media have enabled the rapid and anonymous spread of Ha...
This paper explains the system developed for the Hate Speech Detection (HaSpeeDe) shared task within...
This paper reports on the systems the InriaFBK Team submitted to the EVALITA 2018-Shared Task on Hat...
The increasing popularity of social media platforms like Twitter and Facebook has led to a rise in t...
Hateful and abusive speech presents a major challenge for all online social media platforms. Recent ...
The detection of hate speeches, over social media and online forums, is a relevant task for the rese...
The detection of hate speech in social media is a crucial task. The uncontrolled spread of hate has ...
The task of identifying hate speech in social networks has recently attracted considerable interest ...
We discuss an experiment on comparison of deep learning models for hate speech detection. Online hat...
We describe the systems the RuG Team developed in the context of the Hate Speech Detection Task in I...
International audienceThe massive growth of user-generated web content through blogs, online forums ...
none5siNonostante si osservi un cre-scente interesse per approcci che identi-fichino il linguaggio o...
International audienceHate speech (HS) is legally punished in many countries. Manual moderation of h...
This paper describes our system submission for the GermEval 2018 shared task on the identification o...
University of Minnesota M.S. thesis. May 2020. Major: Computer Science. Advisor: Richard Maclin. 1 c...
The pervasiveness of the Internet and social media have enabled the rapid and anonymous spread of Ha...
This paper explains the system developed for the Hate Speech Detection (HaSpeeDe) shared task within...
This paper reports on the systems the InriaFBK Team submitted to the EVALITA 2018-Shared Task on Hat...
The increasing popularity of social media platforms like Twitter and Facebook has led to a rise in t...
Hateful and abusive speech presents a major challenge for all online social media platforms. Recent ...
The detection of hate speeches, over social media and online forums, is a relevant task for the rese...
The detection of hate speech in social media is a crucial task. The uncontrolled spread of hate has ...
The task of identifying hate speech in social networks has recently attracted considerable interest ...
We discuss an experiment on comparison of deep learning models for hate speech detection. Online hat...
We describe the systems the RuG Team developed in the context of the Hate Speech Detection Task in I...
International audienceThe massive growth of user-generated web content through blogs, online forums ...
none5siNonostante si osservi un cre-scente interesse per approcci che identi-fichino il linguaggio o...
International audienceHate speech (HS) is legally punished in many countries. Manual moderation of h...
This paper describes our system submission for the GermEval 2018 shared task on the identification o...
University of Minnesota M.S. thesis. May 2020. Major: Computer Science. Advisor: Richard Maclin. 1 c...
The pervasiveness of the Internet and social media have enabled the rapid and anonymous spread of Ha...
This paper explains the system developed for the Hate Speech Detection (HaSpeeDe) shared task within...