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A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media

Complex Networks and Their Applications VIII, 2020, Vol.Studies in Computational Intelligence book series (SCI, volume 881), p.928-940 [Peer Reviewed Journal]

Springer Nature Switzerland AG 2020 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1860-949X ;ISBN: 9783030366865 ;ISBN: 3030366863 ;EISSN: 1860-9503 ;EISBN: 9783030366872 ;EISBN: 3030366871 ;DOI: 10.1007/978-3-030-36687-2_77

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  • Title:
    A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media
  • Author: Mozafari, Marzieh ; Farahbakhsh, Reza ; Crespi, Noël
  • Subjects: Artificial Intelligence ; BERT ; Computer Science ; Fine-tuning ; Hate speech detection ; Information Retrieval ; Language modeling ; NLP ; Social and Information Networks ; Social media ; Transfer learning
  • Is Part Of: Complex Networks and Their Applications VIII, 2020, Vol.Studies in Computational Intelligence book series (SCI, volume 881), p.928-940
  • Description: Generated hateful and toxic content by a portion of users in social media is a rising phenomenon that motivated researchers to dedicate substantial efforts to the challenging direction of hateful content identification. We not only need an efficient automatic hate speech detection model based on advanced machine learning and natural language processing, but also a sufficiently large amount of annotated data to train a model. The lack of a sufficient amount of labelled hate speech data, along with the existing biases, has been the main issue in this domain of research. To address these needs, in this study we introduce a novel transfer learning approach based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers). More specifically, we investigate the ability of BERT at capturing hateful context within social media content by using new fine-tuning methods based on transfer learning. To evaluate our proposed approach, we use two publicly available datasets that have been annotated for racism, sexism, hate, or offensive content on Twitter. The results show that our solution obtains considerable performance on these datasets in terms of precision and recall in comparison to existing approaches. Consequently, our model can capture some biases in data annotation and collection process and can potentially lead us to a more accurate model.
  • Publisher: Cham: Springer International Publishing
  • Language: English
  • Identifier: ISSN: 1860-949X
    ISBN: 9783030366865
    ISBN: 3030366863
    EISSN: 1860-9503
    EISBN: 9783030366872
    EISBN: 3030366871
    DOI: 10.1007/978-3-030-36687-2_77
  • Source: Hyper Article en Ligne (HAL) (Open Access)
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