Alternative Speech: Complementary Method to Counter-Narrative for Better Discourse

  • Seungyoon Lee*
  • , Dahyun Jung
  • , Chanjun Park
  • , Seolhwa Lee
  • , Heuiseok Lim
  • *Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Warning: This paper contains examples of stereotypes that may be offensive or upsetting.We introduce the concept of "Alternative Speech"as a new way to directly combat hate speech and complement the limitations of counter-narrative. An alternative speech provides practical alternatives to hate speech in real-world scenarios by offering speech-level corrections to speakers while considering the surrounding context and promoting speakers to reform. Further, an alternative speech can combat hate speech alongside counter-narratives, offering a useful tool to address social issues such as racial discrimination and gender inequality. We propose the new concept and provide detailed guidelines for constructing the necessary dataset. Through discussion, we demonstrate that combining alternative speech and counter-narrative can be a more effective strategy for combating hate speech by complementing specificity and guiding capacity of counter-narrative. This paper presents another perspective for dealing with hate speech, offering viable remedies to complement the constraints of current approaches to mitigating harmful bias.

    Original languageEnglish
    Title of host publicationProceedings - 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023
    EditorsJihe Wang, Yi He, Thang N. Dinh, Christan Grant, Meikang Qiu, Witold Pedrycz
    PublisherIEEE Computer Society
    Pages1438-1442
    Number of pages5
    ISBN (Electronic)9798350381641
    DOIs
    Publication statusPublished - 2023
    Event23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023 - Shanghai, China
    Duration: 2023 Dec 12023 Dec 4

    Publication series

    NameIEEE International Conference on Data Mining Workshops, ICDMW
    ISSN (Print)2375-9232
    ISSN (Electronic)2375-9259

    Conference

    Conference23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023
    Country/TerritoryChina
    CityShanghai
    Period23/12/123/12/4

    Bibliographical note

    Publisher Copyright:
    © 2023 IEEE.

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 5 - Gender Equality
      SDG 5 Gender Equality

    Keywords

    • Counter-Narrative
    • Hate Speech
    • Social Bias

    ASJC Scopus subject areas

    • Computer Science Applications
    • Software

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