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See No Evil, Hear No Evil: Audio-Visual-Textual Cyberbullying Detection

Authors:

Vivek K. SinghAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 2, Issue CSCW

Article No.: 164, Pages 1 - 26

https://doi.org/10.1145/3274433

Published: 01 November 2018 Publication History

Abstract

Emerging multimedia communication apps are allowing for more natural communication and richer user engagement. At the same time, they can be abused to engage in cyberbullying, which can cause significant psychological harm to those affected. Thus, with the growth in multimodal communication platforms, there is an urgent need to devise multimodal methods for cyberbullying detection and prevention. However, there are no existing approaches that use automated audio and video analysis to complement textual analysis. Based on the analysis of a human-labeled cyberbullying data-set of Vine "media sessions' (six-second videos, with audio, and corresponding text comments), we report that: 1) multiple audio and visual features are significantly associated with the occurrence of cyberbullying, and 2) audio and video features complement textual features for more accurate and earlier cyberbullying detection. These results pave the way for more effective cyberbullying detection in emerging multimodal (audio, visual, virtual reality) social interaction spaces.

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Aliyu SKhan SMbodj AOsho OLi LKnijnenburg BCherubini M(2024)Participatory Design to Address Disclosure-Based CyberbullyingProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3660716(1547-1565)Online publication date: 1-Jul-2024
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Park JGracie JAlsoubai ARazi AWisniewski P(2024)Personally Targeted Risk vs. Humor: How Online Risk Perceptions of Youth vs. Third-Party Annotators Differ based on Privately Shared Media on InstagramProceedings of the 23rd Annual ACM Interaction Design and Children Conference10.1145/3628516.3655799(1-13)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3628516.3655799
M R ND VAhmed D M MK J DK J MK V N(2024)An Efficient Approach to Deal with Cyber Bullying using Machine Learning: A Systematic Review2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)10.1109/ic-ETITE58242.2024.10493443(1-8)Online publication date: 22-Feb-2024
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Index Terms

See No Evil, Hear No Evil: Audio-Visual-Textual Cyberbullying Detection
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social media
    2. Empirical studies in collaborative and social computing

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 2, Issue CSCW

November 2018

4104 pages

EISSN:2573-0142

DOI:10.1145/3290265

Editors:
Karrie Karahalios
University of Illinois & Adobe
,
Andrés Monroy-Hernández
Snap Inc.
,
Airi Lampinen
Stockholm University
,
Geraldine Fitzpatrick
Vienna University of Technology

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Published: 01 November 2018

Published in PACMHCI Volume 2, Issue CSCW

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https://dl.acm.org/doi/10.1145/3643834.3660716
Park JGracie JAlsoubai ARazi AWisniewski P(2024)Personally Targeted Risk vs. Humor: How Online Risk Perceptions of Youth vs. Third-Party Annotators Differ based on Privately Shared Media on InstagramProceedings of the 23rd Annual ACM Interaction Design and Children Conference10.1145/3628516.3655799(1-13)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3628516.3655799
M R ND VAhmed D M MK J DK J MK V N(2024)An Efficient Approach to Deal with Cyber Bullying using Machine Learning: A Systematic Review2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)10.1109/ic-ETITE58242.2024.10493443(1-8)Online publication date: 22-Feb-2024
https://doi.org/10.1109/ic-ETITE58242.2024.10493443
Zhu LLu XZhang RHuang B(2024)A Multimodal Correlation and Interaction-based Method for Cyberbullying Detection2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650748(1-8)Online publication date: 30-Jun-2024
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Ahmad Al-Khasawneh MFaheem MAbdulsalam Alarood AHabibullah SAlsolami E(2024)Toward Multi-Modal Approach for Identification and Detection of Cyberbullying in Social NetworksIEEE Access10.1109/ACCESS.2024.342013112(90158-90170)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3420131
Festus Ayetiran EÖzgöbek Ö(2024)A Review of Deep Learning Techniques for Multimodal Fake News and Harmful Languages DetectionIEEE Access10.1109/ACCESS.2024.340625812(76133-76153)Online publication date: 2024
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Teng TVarathan KCrestani F(2024)A comprehensive review of cyberbullying-related content classification in online social mediaExpert Systems with Applications10.1016/j.eswa.2023.122644244(122644)Online publication date: Jun-2024
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Mishra ASinha SGeorge C(2024)Shielding against online harmEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108241133:PDOnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108241
Alfurayj HHurtado BLutfi SRana T(2024)Exploring bystander contagion in cyberbully detection: a systematic reviewJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-024-04831-wOnline publication date: 17-Jul-2024
https://doi.org/10.1007/s12652-024-04831-w
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