University of Oulu

Djamila Romaissa Beddiar, Md Saroar Jahan, Mourad Oussalah, Data expansion using back translation and paraphrasing for hate speech detection, Online Social Networks and Media, Volume 24, 2021, 100153, ISSN 2468-6964,

Data expansion using back translation and paraphrasing for hate speech detection

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Author: Beddiar, Djamila Romaissa1; Jahan, Md Saroar1; Oussalah, Mourad1
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.6 MB)
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Language: English
Published: Elsevier, 2021
Publish Date: 2021-09-06


With proliferation of user generated contents in social media platforms, establishing mechanisms to automatically identify toxic and abusive content becomes a prime concern for regulators, researchers, and society. Keeping the balance between freedom of speech and respecting each other dignity is a major concern of social media platform regulators. Although, automatic detection of offensive content using deep learning approaches seems to provide encouraging results, training deep learning-based models requires large amounts of high-quality labeled data, which is often missing. In this regard, we present in this paper a new deep learning-based method that fuses a Back Translation method, and a Paraphrasing technique for data augmentation. Our pipeline investigates different word-embedding-based architectures for classification of hate speech. The back translation technique relies on an encoder–decoder architecture pre-trained on a large corpus and mostly used for machine translation. In addition, paraphrasing exploits the transformer model and the mixture of experts to generate diverse paraphrases. Finally, LSTM, and CNN are compared to seek enhanced classification results. We evaluate our proposal on five publicly available datasets; namely, AskFm corpus, Formspring dataset, Warner and Waseem dataset, Olid, and Wikipedia toxic comments dataset. The performance of the proposal together with comparison to some related state-of-art results demonstrate the effectiveness and soundness of our proposal.

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Series: Online social networks and media
ISSN: 2468-6964
ISSN-E: 2468-6964
ISSN-L: 2468-6964
Volume: 24
Article number: 100153
DOI: 10.1016/j.osnem.2021.100153
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work is supported by the European Young-sters Resilience through Serious Games , under the Internal Security Fund-Police action: 823701-ISFP-2017-AG-RAD grant, which is gratefully acknowledged.
Copyright information: © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (