IDEAS home Printed from https://ideas.repec.org/a/igg/jitwe0/v17y2022i1p1-24.html
   My bibliography  Save this article

Relationship Between Personality Patterns and Harmfulness: Analysis and Prediction Based on Sentence Embedding

Author

Listed:
  • Kazuyuki Matsumoto

    (Tokushima University, Japan)

  • Ryota Kishima

    (Tokushima University, Japan)

  • Seiji Tsuchiya

    (Doshisha University, Japan)

  • Tomoki Hirobayashi

    (Yamada Denken Co., Ltd., Japan)

  • Minoru Yoshida

    (Tokushima University, Japan)

  • Kenji Kita

    (Tokushima University, Japan)

Abstract

This paper hypothesize that harmful utterances need to be judged in context of whole sentences, and extract features of harmful expressions using a general-purpose language model. Based on the extracted features, we propose a method to predict the presence or absence of harmful categories. In addition, the authors believe that it is possible to analyze users who incite others by combining this method with research on analyzing the personality of the speaker from statements on social networking sites. The results confirmed that the proposed method can judge the possibility of harmful comments with higher accuracy than simple dictionary-based models or models using a distributed representations of words. The relationship between personality patterns and harmful expressions was also confirmed by an analysis based on a harmful judgment model.

Suggested Citation

  • Kazuyuki Matsumoto & Ryota Kishima & Seiji Tsuchiya & Tomoki Hirobayashi & Minoru Yoshida & Kenji Kita, 2022. "Relationship Between Personality Patterns and Harmfulness: Analysis and Prediction Based on Sentence Embedding," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 17(1), pages 1-24, January.
    • Handle: RePEc:igg:jitwe0:v:17:y:2022:i:1:p:1-24
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJITWE.298654
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Geetika Sarna & M. P. S. Bhatia, 2020. "Structure-Based Analysis of Different Categories of Cyberbullying in Dynamic Social Network," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 14(3), pages 1-17, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jitwe0:v:17:y:2022:i:1:p:1-24. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.