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Directions in abusive language training data, a systematic review: Garbage in, garbage out

PloS one, 2020-12, Vol.15 (12), p.e0243300-e0243300 [Peer Reviewed Journal]

COPYRIGHT 2020 Public Library of Science ;COPYRIGHT 2020 Public Library of Science ;2020 Vidgen, Derczynski 2020 Vidgen, Derczynski ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0243300 ;PMID: 33370298

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  • Title:
    Directions in abusive language training data, a systematic review: Garbage in, garbage out
  • Author: Vidgen, Bertie ; Derczynski, Leon
  • Grabar, Natalia
  • Subjects: Biology and Life Sciences ; Computer and Information Sciences ; Databases, Factual ; Electronic data processing ; Harassment (Law) ; Hate ; Hate speech ; Humans ; Language ; Machine Learning ; Science Policy ; Social Sciences
  • Is Part Of: PloS one, 2020-12, Vol.15 (12), p.e0243300-e0243300
  • Description: Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness and increasingly high performance. Making effective detection systems for abusive content relies on having the right training datasets, reflecting a widely accepted mantra in computer science: Garbage In, Garbage Out. However, creating training datasets which are large, varied, theoretically-informed and that minimize biases is difficult, laborious and requires deep expertise. This paper systematically reviews 63 publicly available training datasets which have been created to train abusive language classifiers. It also reports on creation of a dedicated website for cataloguing abusive language data hatespeechdata.com. We discuss the challenges and opportunities of open science in this field, and argue that although more dataset sharing would bring many benefits it also poses social and ethical risks which need careful consideration. Finally, we provide evidence-based recommendations for practitioners creating new abusive content training datasets.
  • Publisher: United States: Public Library of Science
  • Language: English
  • Identifier: ISSN: 1932-6203
    EISSN: 1932-6203
    DOI: 10.1371/journal.pone.0243300
    PMID: 33370298
  • Source: Public Library of Science (PLoS) Journals Open Access
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    AUTh Library subscriptions: ProQuest Central
    MEDLINE
    PubMed Central
    DOAJ Directory of Open Access Journals
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