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Article dans une revue

A Multilingual Evaluation for Online Hate Speech Detection

Michele Corazza Stefano Menini Elena Cabrio 1 Sara Tonelli Serena Villata 2, 1
2 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 : The increasing popularity of social media platforms like Twitter and Facebook has led to a rise in the presence of hate and aggressive speech on these platforms. Despite the number of approaches recently proposed in the Natural Language Processing research area for detecting these forms of abusive language, the issue of identifying hate speech at scale is still an unsolved problem. In this paper, we propose a robust neural architecture which is shown to perform in a satisfactory way across different languages, namely English, Italian and German. We address an extensive analysis of the obtained experimental results over the three languages to gain a better understanding of the contribution of the different components employed in the system, both from the architecture point of view (i.e., Long Short Term Memory, Gated Recurrent Unit, and bidirectional Long Short Term Memory) and from the feature selection point of view (i.e., ngrams, social network specific features, emotion lexica, emojis, word embeddings). To address such in-depth analysis, we use three freely available datasets for hate speech detection on social media on English, Italian and German.
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Contributeur : Serena Villata <>
Soumis le : mardi 20 octobre 2020 - 14:19:50
Dernière modification le : mercredi 21 octobre 2020 - 03:40:31

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Michele Corazza, Stefano Menini, Elena Cabrio, Sara Tonelli, Serena Villata. A Multilingual Evaluation for Online Hate Speech Detection. ACM Transactions on Internet Technology, Association for Computing Machinery, 2020, 20 (2), pp.1-22. ⟨10.1145/3377323⟩. ⟨hal-02972184⟩

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