research-article

Cyber Bullying Detection Using Social and Textual Analysis

Authors:

Qianjia Huang,

Vivek Kumar Singh,

Pradeep Kumar AtreyAuthors Info & Claims

SAM '14: Proceedings of the 3rd International Workshop on Socially-Aware Multimedia

Pages 3 - 6

https://doi.org/10.1145/2661126.2661133

Published: 07 November 2014 Publication History

Get Access

Abstract

Cyber Bullying, which often has a deeply negative impact on the victim, has grown as a serious issue among adolescents. To understand the phenomenon of cyber bullying, experts in social science have focused on personality, social relationships and psychological factors involving both the bully and the victim. Recently computer science researchers have also come up with automated methods to identify cyber bullying messages by identifying bullying-related keywords in cyber conversations. However, the accuracy of these textual feature based methods remains limited. In this work, we investigate whether analyzing social network features can improve the accuracy of cyber bullying detection. By analyzing the social network structure between users and deriving features such as number of friends, network embeddedness, and relationship centrality, we find that the detection of cyber bullying can be significantly improved by integrating the textual features with social network features.

References

[1]

Y. Altshuler, M. Fire, E. Shmueli, Y. Elovici, A. Bruckstein, A. S. Pentland, and D. Lazer. The social ampli_er - reaction of human communities to emergencies. Journal of Statistical Physics, 152(3):399--418, 2013.

Crossref

Google Scholar

[2]

D. C. Camp_eld. Cyber bullying and victimization: Psychosocial characteristics of bullies, victims, and bully/victims. ProQuest, 2008.

Google Scholar

[3]

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

Crossref

Google Scholar

[4]

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

Digital Library

Google Scholar

[5]

T. D_avid-Barrett and R. Dunbar. Processing power limits social group size: computational evidence for the cognitive costs of sociality. Proceedings of the Royal Society B: Biological Sciences, 280(1765), 2013.

Google Scholar

[6]

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

Google Scholar

[7]

V. Nahar, X. Li, and C. Pang. An e_ective approach for cyberbullying detection. Communications in Information Science and Management Engineering, 3(5):238--247, 2013.

Google Scholar

[8]

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

Google Scholar

Cited By

View all

Kumar CArun Kumar KGupta SSardar T(2024)Cyberbullying detection based on the fusion of DistilBERT and SIMHASH Technique2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA)10.1109/AIMLA59606.2024.10531427(1-4)Online publication date: 15-Mar-2024
https://doi.org/10.1109/AIMLA59606.2024.10531427
Chu QQin YYang LYang X(2024)Game-theoretic modeling and analysis of cyberbullying spreading on OSNsInformation Sciences10.1016/j.ins.2023.120067(120067)Online publication date: Jan-2024
https://doi.org/10.1016/j.ins.2023.120067
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
https://doi.org/10.1016/j.eswa.2023.122644
Show More Cited By

Index Terms

Cyber Bullying Detection Using Social and Textual Analysis
1. Applied computing
  1. Law, social and behavioral sciences

Recommendations

Cyber offending predictors and pathways in middle adolescence: Evidence from the UK Millennium Cohort Study
Abstract
Despite the pervasiveness of cyber crime victimisation, knowledge is limited regarding the prevalence, characteristics and pathways of offenders. The present study examines predictors of self-reported engagement in cyber crime in middle ...
Highlights
- Predictors of self-reported engagement in cyber offending in adolescence were examined in a large UK longitudinal dataset.
- Prevalence of self-reported cyber offending was 5.6% at age 14 and 3.8% at age 17, but only 1.1% persisted at ...
Adolescents and Cyber Bullying: The Precaution Adoption Process Model

A survey of adolescents (N = 1,488) documented Facebook use and experience with cyber bullying. The study found that 84 % of adolescents (middle school through college undergraduates) use Facebook, and that most users log on daily. While 30 % of the ...
Perceptions and attributions of bystanders to cyber bullying

Bystanders play a critical role in the maintenance or reduction of bullying behavior. The potentially unlimited audience in the online world suggests that the role of bystanders may be particularly important in cyber bullying. However, little is known ...

Comments

Information & Contributors

Information

Published In

SAM '14: Proceedings of the 3rd International Workshop on Socially-Aware Multimedia

November 2014

28 pages

ISBN:9781450331241

DOI:10.1145/2661126

Program Chairs:
Pablo Cesar
CWI: Centrum Wiskunde & Informatica, The Netherlands
,
David A. Shamma
Yahoo! Research, USA
,
Matthew Cooper
FXPAL, USA
,
Aisling Kelliher
Carnegie Mellon University, USA

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: 07 November 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

University at Albany
Natural Sciences and Engineering Research Council of Canada

Conference

MM '14

Sponsor:

SIGMM

MM '14: 2014 ACM Multimedia Conference

November 7, 2014

Florida, Orlando, USA

Acceptance Rates

SAM '14 Paper Acceptance Rate 3 of 5 submissions, 60%;

Overall Acceptance Rate 36 of 59 submissions, 61%

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

145
Total Citations
View Citations
1,985
Total Downloads

Downloads (Last 12 months)89
Downloads (Last 6 weeks)13

Reflects downloads up to 23 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Kumar CArun Kumar KGupta SSardar T(2024)Cyberbullying detection based on the fusion of DistilBERT and SIMHASH Technique2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA)10.1109/AIMLA59606.2024.10531427(1-4)Online publication date: 15-Mar-2024
https://doi.org/10.1109/AIMLA59606.2024.10531427
Chu QQin YYang LYang X(2024)Game-theoretic modeling and analysis of cyberbullying spreading on OSNsInformation Sciences10.1016/j.ins.2023.120067(120067)Online publication date: Jan-2024
https://doi.org/10.1016/j.ins.2023.120067
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
https://doi.org/10.1016/j.eswa.2023.122644
R SJ A(2024)Classification of cyberbullying messages using text, image and audio in social networks: a deep learning approachMultimedia Tools and Applications10.1007/s11042-023-15538-z83:1(2237-2266)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15538-z
Zhu JRuan YChang JSun WWan HLong JLuo C(2024)Deep Prompt Multi-task Network for Abuse Language DetectionPattern Recognition10.1007/978-3-031-78107-0_16(249-263)Online publication date: 2-Dec-2024
https://doi.org/10.1007/978-3-031-78107-0_16
Sharma KSingh K(2023)Psychological Study of Cyber-Bullying Against Adolescent Girls in India Using TwitterInternational Journal of Cyber Behavior, Psychology and Learning10.4018/IJCBPL.32786713:1(1-22)Online publication date: 11-Aug-2023
https://dl.acm.org/doi/10.4018/IJCBPL.327867
Fkih FMoulahi TAlabdulatif A(2023)Machine Learning Model for Offensive Speech Detection in Online Social Networks Slang ContentWSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS10.37394/23209.2023.20.220(7-15)Online publication date: 17-Jan-2023
https://doi.org/10.37394/23209.2023.20.2
Huang JDing RZheng YWu XChen SJin X(2023)Does Part of Speech Have an Influence on Cyberbullying Detection?Analytics10.3390/analytics30100013:1(1-13)Online publication date: 21-Dec-2023
https://doi.org/10.3390/analytics3010001
Woo WChua HGan M(2023)Cyberbullying Conceptualization, Characterization and Detection in Social Media – A Systematic Literature ReviewInternational Journal on Perceptive and Cognitive Computing10.31436/ijpcc.v9i1.3749:1(101-121)Online publication date: 28-Jan-2023
https://doi.org/10.31436/ijpcc.v9i1.374
Saeed RAfzal HRauf SIltaf N(2023)Detection of Offensive Language and ITS Severity for Low Resource LanguageACM Transactions on Asian and Low-Resource Language Information Processing10.1145/358047622:6(1-27)Online publication date: 19-Jan-2023
https://dl.acm.org/doi/10.1145/3580476
Show More Cited By

View Options

Login options

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

Cited By

Index Terms

Recommendations

Cyber offending predictors and pathways in middle adolescence: Evidence from the UK Millennium Cohort Study

Adolescents and Cyber Bullying: The Precaution Adoption Process Model

Perceptions and attributions of bystanders to cyber bullying