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Volume 9 Issue 8
Aug.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(8): 1384-1405
S. W. Wang, X. Q. Zhu, W. P. Ding, and A. A. Yengejeh, “Cyberbullying and cyberviolence detection: A triangular user-activity-content view,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1384–1405, Aug. 2022. doi: 10.1109/JAS.2022.105740
Citation: S. W. Wang, X. Q. Zhu, W. P. Ding, and A. A. Yengejeh, “Cyberbullying and cyberviolence detection: A triangular user-activity-content view,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1384–1405, Aug. 2022. doi: 10.1109/JAS.2022.105740
shu

Cyberbullying and Cyberviolence Detection: A Triangular User-Activity-Content View

doi: 10.1109/JAS.2022.105740
  • 1.

    Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431 USA

  • 2.

    School of Information Science and Technology, Nantong University, Nantong 226019, China

Funds:  This work was partially supported by the U.S. National Science Foundation (CNS-1828181, IIS-1763452), and by a seed grant of the College of Engineering and Computer Science, Florida Atlantic University
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    Abstract
  • Recent years have witnessed the increasing popularity of mobile and networking devices, as well as social networking sites, where users engage in a variety of activities in the cyberspace on a daily and real-time basis. While such systems provide tremendous convenience and enjoyment for users, malicious usages, such as bullying, cruelty, extremism, and toxicity behaviors, also grow noticeably, and impose significant threats to individuals and communities. In this paper, we review computational approaches for cyberbullying and cyberviolence detection, in order to understand two major factors: 1) What are the defining features of online bullying users, and 2) How to detect cyberbullying and cyberviolence. To achieve the goal, we propose a user-activities-content (UAC) triangular view, which defines that users in the cyberspace are centered around the UAC triangle to carry out activities and generate content. Accordingly, we categorize cyberbully features into three main categories: 1) User centered features, 2) Content centered features, and 3) Activity centered features. After that, we review methods for cyberbully detection, by taking supervised, unsupervised, transfer learning, and deep learning, etc., into consideration. The UAC centered view provides a coherent and complete summary about features and characteristics of online users (their activities), approaches to detect bullying users (and malicious content), and helps defend cyberspace from bullying and toxicity.

     

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    Highlights

    • A comprehensive review of computational approaches for cyberbullying and cyberviolence detection
    • A User-Activities-Content (UAC) triangle view of the key factors in cyberbullying
    • Important features and their interactions in cyberbullying detection
    • Machine learning methods for cyberbullying detection

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