Skip to Main content Skip to Navigation
Conference papers

InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks

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 : In this paper, we describe two systems for predicting message-level offensive language in German tweets: one discriminates between offensive and not offensive messages, and the second performs a fine-grained classification by recognizing also classes of offense. Both systems are based on the same approach, which builds upon Recurrent Neural Networks used with the following features: word embeddings, emoji embeddings and social-network specific features. The model is able to combine word-level information and tweet-level information in order to perform the classification tasks.
Complete list of metadata

Cited literature [15 references]  Display  Hide  Download
Contributor : Michele Corazza Connect in order to contact the contributor
Submitted on : Friday, October 26, 2018 - 2:13:52 PM
Last modification on : Friday, January 21, 2022 - 3:10:27 AM
Long-term archiving on: : Sunday, January 27, 2019 - 2:04:42 PM


Files produced by the author(s)


  • HAL Id : hal-01906096, version 1



Michele Corazza, Stefano Menini, Pinar Arslan, Rachele Sprugnoli, Elena Cabrio, et al.. InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks. GermEval 2018 Workshop, Sep 2018, Vienna, Austria. ⟨hal-01906096⟩



Record views


Files downloads