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Challenges of Hate Speech Detection in Social Media: Data Scarcity, and Leveraging External Resources

SN computer science, 2021-04, Vol.2 (2), Article 95 [Peer Reviewed Journal]

The Author(s) 2021 ;ISSN: 2662-995X ;ISSN: 2661-8907 ;EISSN: 2661-8907 ;DOI: 10.1007/s42979-021-00457-3

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  • Title:
    Challenges of Hate Speech Detection in Social Media: Data Scarcity, and Leveraging External Resources
  • Author: Kovács, György ; Alonso, Pedro ; Saini, Rajkumar
  • Subjects: BERT ; Computer Imaging ; Computer Science ; Computer Systems Organization and Communication Networks ; Data Structures and Information Theory ; Deep language processing ; Hate speech ; Information Systems and Communication Service ; Machine Learning ; Maskininlärning ; Original Research ; Pattern Recognition and Graphics ; Social Media Analytics and its Evaluation ; Software Engineering/Programming and Operating Systems ; Transfer learning ; Vision ; Vocabulary augmentation
  • Is Part Of: SN computer science, 2021-04, Vol.2 (2), Article 95
  • Description: The detection of hate speech in social media is a crucial task. The uncontrolled spread of hate has the potential to gravely damage our society, and severely harm marginalized people or groups. A major arena for spreading hate speech online is social media. This significantly contributes to the difficulty of automatic detection, as social media posts include paralinguistic signals (e.g. emoticons, and hashtags), and their linguistic content contains plenty of poorly written text. Another difficulty is presented by the context-dependent nature of the task, and the lack of consensus on what constitutes as hate speech, which makes the task difficult even for humans. This makes the task of creating large labeled corpora difficult, and resource consuming. The problem posed by ungrammatical text has been largely mitigated by the recent emergence of deep neural network (DNN) architectures that have the capacity to efficiently learn various features. For this reason, we proposed a deep natural language processing (NLP) model—combining convolutional and recurrent layers—for the automatic detection of hate speech in social media data. We have applied our model on the HASOC2019 corpus, and attained a macro F1 score of 0.63 in hate speech detection on the test set of HASOC. The capacity of DNNs for efficient learning, however, also means an increased risk of overfitting. Particularly, with limited training data available (as was the case for HASOC). For this reason, we investigated different methods for expanding resources used. We have explored various opportunities, such as leveraging unlabeled data, similarly labeled corpora, as well as the use of novel models. Our results showed that by doing so, it was possible to significantly increase the classification score attained.
  • Publisher: Singapore: Springer Singapore
  • Language: English
  • Identifier: ISSN: 2662-995X
    ISSN: 2661-8907
    EISSN: 2661-8907
    DOI: 10.1007/s42979-021-00457-3
  • Source: SWEPUB Freely available online
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