research-article

Cyberbullying detection using parent-child relationship between comments

Authors:

Junpei Kawamoto,

Kouichi SakuraiAuthors Info & Claims

iiWAS '16: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services

Pages 325 - 334

https://doi.org/10.1145/3011141.3011182

Published: 28 November 2016 Publication History

Abstract

Cyberbullying is a underlying problem in social networking service, threatening users' mental and physical health. Previous research on automated cyberbullying detection is mostly textual or social based methods. Cyberbullying content is identified through a set of textual features within the content in the former method and through social information surrounding the content in the latter method. Those methods can not cater different cyberbullying standard for individual SNS user since each content is evaluated using same features. Therefore, in this article we propose a automated cyberbullying detection method that utilises the parent-child relationship between comments to capture the reaction from a third party to detect cyberbullying comments. We were able to improve the effectiveness of cyberbullying detection using only publicly available data.

References

[1]

S. Bastiaensens, H. Vandebosch, K. Poels, K. Van Cleemput, A. Desmet, and I. De Bourdeaudhuij. Cyberbullying on social network sites. an experimental study into bystanders' behavioural intentions to help the victim or reinforce the bully. Computers in Human Behavior, 31:259--271, 2014.

Digital Library

[2]

S. Bird. Nltk: the natural language toolkit. In Proceedings of the COLING/ACL on Interactive presentation sessions, pages 69--72. Association for Computational Linguistics, 2006.

Digital Library

[3]

C. Burger, D. Strohmeier, N. Spröber, S. Bauman, and K. Rigby. How teachers respond to school bullying: An examination of self-reported intervention strategy use, moderator effects, and concurrent use of multiple strategies. Teaching and Teacher Education, 51:191--202, 2015.

[4]

M. Campbell, B. Spears, P. Slee, D. Butler, and S. Kift. Victims' perceptions of traditional and cyberbullying, and the psychosocial correlates of their victimisation. Emotional and Behavioural Difficulties, 17(3--4):389--401, 2012.

[5]

M. Cha, H. Kwak, P. Rodriguez, Y.-Y. Ahn, and S. Moon. I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system. In Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, pages 1--14. ACM, 2007.

Digital Library

[6]

N. V. Chawla. Data mining for imbalanced datasets: An overview. In Data mining and knowledge discovery handbook, pages 853--867. Springer, 2005.

[7]

M. Dadvar, F. M. de Jong, R. Ordelman, and R. Trieschnigg. Improved cyberbullying detection using gender information. 2012.

[8]

M. Dadvar, D. Trieschnigg, R. Ordelman, and F. de Jong. Improving cyberbullying detection with user context. In European Conference on Information Retrieval, pages 693--696. Springer, 2013.

Digital Library

[9]

K. Dinakar, R. Reichart, and H. Lieberman. Modeling the detection of textual cyberbullying. The Social Mobile Web, 11:02, 2011.

[10]

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10--18, 2009.

Digital Library

[11]

Q. Huang, V. K. Singh, and P. K. Atrey. Cyber bullying detection using social and textual analysis. In Proceedings of the 3rd International Workshop on Socially-Aware Multimedia, pages 3--6. ACM, 2014.

Digital Library

[12]

C. J. Hutto and E. Gilbert. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Eighth International AAAI Conference on Weblogs and Social Media, 2014.

[13]

C. Langos. Cyberbullying: The challenge to define. Cyberpsychology, Behavior, and Social Networking, 15(6):285--289, 2012.

[14]

P. O'CONNELL, D. Pepler, and W. Craig. Peer involvement in bullying: Insights and challenges for intervention. Journal of adolescence, 22(4):437--452, 1999.

[15]

K. Reynolds, A. Kontostathis, and L. Edwards. Using machine learning to detect cyberbullying. In Machine Learning and Applications and Workshops (ICMLA), 2011 10th International Conference on, volume 2, pages 241--244. IEEE, 2011.

Digital Library

[16]

T. D. Smedt and W. Daelemans. Pattern for python. Journal of Machine Learning Research, 13(Jun):2063--2067, 2012.

Digital Library

[17]

D. Yin, Z. Xue, L. Hong, B. D. Davison, A. Kontostathis, and L. Edwards. Detection of harassment on web 2.0. Proceedings of the Content Analysis in the WEB, 2:1--7, 2009.

Cited By

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
https://dl.acm.org/doi/10.1145/3643834.3660716
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
Kumar MHimanshu Choudhary VNishal Y(2023)Using Discussion Thread Context in Sentiment Analysis for Improving Cyberbullying Detection2023 3rd Asian Conference on Innovation in Technology (ASIANCON)10.1109/ASIANCON58793.2023.10270005(1-4)Online publication date: 25-Aug-2023
https://doi.org/10.1109/ASIANCON58793.2023.10270005
Show More Cited By

Index Terms

Cyberbullying detection using parent-child relationship between comments
1. Applied computing
  1. Law, social and behavioral sciences

Recommendations

Loneliness, parent-child communication and cyberbullying victimization among Spanish youths

Cyberbullying has been recognized as an important risk factor for mental health. Few studies have analyzed relationships between family variables and cyberbullying victimization. This ex post facto study analyze the relationships between loneliness, ...
Prevalence of cyberbullying and predictors of cyberbullying perpetration among Korean adolescents

This study aimed to investigate the prevalence of cyberbullying and factors in cyberbullying perpetration with a national sample of 4000 adolescents selected through multi-stage cluster sampling. The respondents were 2166 boys (54.1%) and 1834 girls (...
Parental mediation, cyberbullying, and cybertrolling

Researchers are concerned with identifying the risk and protective factors associated with adolescents' involvement in cyberharassment. One such factor is parental mediation of children's electronic technology use. Little attention has been given to how ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

iiWAS '16: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services

November 2016

528 pages

ISBN:9781450348072

DOI:10.1145/3011141

General Chair:
Gabriele Anderst-Kotsis
Johannes Kepler University Linz, Austria

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 November 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

iiWAS '16

iiWAS '16: 18th International Conference on Information Integration and Web-based Applications and Services

November 28 - 30, 2016

Singapore, Singapore

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
380
Total Downloads

Downloads (Last 12 months)18
Downloads (Last 6 weeks)0

Reflects downloads up to 15 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

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
https://dl.acm.org/doi/10.1145/3643834.3660716
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
Kumar MHimanshu Choudhary VNishal Y(2023)Using Discussion Thread Context in Sentiment Analysis for Improving Cyberbullying Detection2023 3rd Asian Conference on Innovation in Technology (ASIANCON)10.1109/ASIANCON58793.2023.10270005(1-4)Online publication date: 25-Aug-2023
https://doi.org/10.1109/ASIANCON58793.2023.10270005
Sawhney RAgarwal SNeerkaje AAletras NNakov PFlek LAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Towards Suicide Ideation Detection Through Online Conversational ContextProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532068(1716-1727)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532068
Saha UMahmud MKeya MLucky EKhushbu SNoori SSyed M(2022)Exploring Public Attitude Towards Children by Leveraging Emoji to Track Out Sentiment Using Distil-BERT a Fine-Tuned ModelThird International Conference on Image Processing and Capsule Networks10.1007/978-3-031-12413-6_26(332-346)Online publication date: 29-Jul-2022
https://doi.org/10.1007/978-3-031-12413-6_26
Balayn AYang JSzlavik ZBozzon A(2021)Automatic Identification of Harmful, Aggressive, Abusive, and Offensive Language on the Web: A Survey of Technical Biases Informed by Psychology LiteratureACM Transactions on Social Computing10.1145/34791584:3(1-56)Online publication date: 25-Oct-2021
https://dl.acm.org/doi/10.1145/3479158
Kim SRazi AStringhini GWisniewski PDe Choudhury M(2021)A Human-Centered Systematic Literature Review of Cyberbullying Detection AlgorithmsProceedings of the ACM on Human-Computer Interaction10.1145/34760665:CSCW2(1-34)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3476066
Iivari NVentä-Olkkonen LSharma SMolin-Juustila TKinnunen EKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)CHI Against BullyingProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445282(1-17)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445282
Wang KXiong QWu CGao MYu Y(2020)Multi-modal cyberbullying detection on social networks2020 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN48605.2020.9206663(1-8)Online publication date: Jul-2020
https://doi.org/10.1109/IJCNN48605.2020.9206663
DiFranzo DTaylor SKazerooni FWherry OBazarova NMandryk RHancock MPerry MCox A(2018)Upstanding by DesignProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3173785(1-12)Online publication date: 21-Apr-2018
https://dl.acm.org/doi/10.1145/3173574.3173785

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents