Zhang, Z., Robinson, D. and Tepper, J. (2018) Detecting hate speech on Twitter using a convolution-GRU based deep neural network. In: Gangemi, A., Navigli, R., Vidal, M.E., Hitzler, P., Troncy, R., Hollink, L., Tordai, A. and Alam, M., (eds.) ESWC 2018: The semantic web. ESWC 2018, 03-07 Jun 2018, Heraklion, Greece. Lecture Notes in Computer Science, 10843 . Springer Verlag , pp. 745-760. ISBN 978-3-319-93417-4
Abstract
In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, and empirical research. Despite a large number of emerging scientific studies to address the problem, a major limitation of existing work is the lack of comparative evaluations, which makes it difficult to assess the contribution of individual works. This paper introduces a new method based on a deep neural network combining convolutional and gated recurrent networks. We conduct an extensive evaluation of the method against several baselines and state of the art on the largest collection of publicly available Twitter datasets to date, and show that compared to previously reported results on these datasets, our proposed method is able to capture both word sequence and order information in short texts, and it sets new benchmark by outperforming on 6 out of 7 datasets by between 1 and 13% in F1. We also extend the existing dataset collection on this task by creating a new dataset covering different topics.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2018 Springer International Publishing AG, part of Springer Nature. This is an author produced version of a paper subsequently published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Mar 2018 15:17 |
Last Modified: | 24 Jul 2018 09:35 |
Published Version: | https://doi.org/10.1007/978-3-319-93417-4_48 |
Status: | Published |
Publisher: | Springer Verlag |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-93417-4_48 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:128405 |