More Web Proxy on the site http://driver.im/

research-article

Using Fuzzy Fingerprints for Cyberbullying Detection in Social Networks

Authors:

Joao P. Carvalho,

Ricardo Ribeiro,

Luisa CoheurAuthors Info & Claims

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Pages 1 - 7

https://doi.org/10.1109/FUZZ-IEEE.2018.8491557

Published: 08 July 2018 Publication History

Abstract

As cyberbullying becomes more and more frequent in social networks, automatically detecting it and pro-actively acting upon it becomes of the utmost importance. In this work, we study how a recent technique with proven success in similar tasks, Fuzzy Fingerprints, performs when detecting textual cyberbullying in social networks. Despite being commonly treated as binary classification task, we argue that this is in fact a retrieval problem where the only relevant performance is that of retrieving cyberbullying interactions. Experiments show that the Fuzzy Fingerprints slightly outperforms baseline classifiers when tested in a close to real life scenario, where cyberbullying instances are rarer than those without cyberbullying.

References

[1]

D. Richardson and C. F. Hiu, Ending the torment: tackling bullying from the schoolyard to cyberspace. 2016.

[2]

B. Belsey, “Cyberbullying: An emerging threat to the always on generation,” Bullying Org. Canada, pp. 1–9, 2005.

[3]

J. Amado, A. Matos, and T. Pessoa, “Cyberbullying: um desafio à investigação e à formação,” Revista Interacções, vol. 13, no. 13, pp. 301–326, 2009.

[4]

R. Slonje, P. K. Smith, and A. Frisén, “The nature of cyberbullying, and strategies for prevention,” Computers in Human Behavior, vol. 29, no. 1, pp. 26–32, 2013.

Digital Library

[5]

R. Slonje and P. K. Smith, “Cyberbullying: Another main type of bullying?: Personality and Social Sciences,” Scandinavian Journal of Psychology, vol. 49, no. 2, pp. 147–154, 2008.

[6]

Q. Li, “A cross-cultural comparison of adolescents’ experience related to cyberbullying,” Educational Research, vol. 50, no. 3, pp. 223–234, 2008.

[7]

H. Jang, J. Song, and R. Kim, “Does the offline bully-victimization influence cyberbullying behavior among youths? Application of General Strain Theory,” Computers in Human Behavior, vol. 31, no. 1, pp. 85–93, 2014.

Digital Library

[8]

P. C. Ferreira, A. M. V. Sima, A. Ferreira, S. Souza, and S. Francisco, “Student bystander behavior and cultural issues in cyberbullying: When actions speak louder than words,” Computers in Human Behavior, vol. 60, pp. 301–311, 2016.

Digital Library

[9]

J. Bayzick, A. Kontostathis, and L. Edwards, “Detecting the Presence of Cyberbullying Using Computer Software,” Springer, no. December, pp. 11–12, 2011.

[10]

K. Reynolds, A. Kontostathis, and L. Edwards, “Using machine learning to detect cyberbullying,” Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011, vol. 2, pp. 241–244, 2011.

[11]

K. Dinakar, R. Reichart, and H. Lieberman, “Modeling the Detection of Textual Cyberbullying,” Association for the Advancement of Artificial Intelligence, pp. 11–17, 2011.

[12]

M. Dadvar, D. Trieschnigg, R. Ordelman, and F. de Jong, “Improving Cyberbullying Detection with User Context,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7814 LNCS, pp. 693–696, 2013.

[13]

V. S. Chavan and S. S. Shylaja, “Machine learning approach for detection of cyber-aggressive comments by peers on social media network,” 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, pp. 2354–2358, 2015.

[14]

Q. Huang, V. K. Singh, and P. K. Atrey, “Cyber Bullying Detection Using Social and Textual Analysis,” Proceedings of the 3rd International Workshop on Socially-Aware Multimedia - SAM ’14, pp. 3–6, 2014.

[15]

V. Nahar, X. Li, and C. Pang, “An Effective Approach for Cyberbullying Detection,” Communications in Information Science and Management Engineering, vol. 3, no. 5, pp. 238–247, 2014.

[16]

K. Dinakar, R. Picard, and H. Lieberman, “Common sense reasoning for detection, prevention, and mitigation of cyberbullying,” IJCAI International Joint Conference on Artificial Intelligence, vol. 2015-January, no. 3, pp. 4168–4172, 2015.

[17]

R. Zhao, A. Zhou, and K. Mao, “Automatic detection of cyberbullying on social networks based on bullying features,” Proceedings of the 17th International Conference on Distributed Computing and Networking - ICDCN ’16, pp. 1–6, 2016.

[18]

M. A. Al-Garadi, K. D. Varathan, and S. D. Ravana, “Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network,” Computers in Human Behavior, vol. 63, pp. 433–443, 2016.

Digital Library

[19]

X. Zhang, J. Tong, N. Vishwamitra, E. Whittaker, J. P. Mazer, R. Kowalski, H. Hu, F. Luo, J. Macbeth, and E. Dillon, “Cyberbullying detection with a pronunciation based convolutional neural network,” Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, no. February 2017, pp. 740–745, 2017.

[20]

A. Kasture, A predictive model to detect online cyberbullying. PhD thesis, Auckland University of Technology, 2015.

[21]

N. Vishwamitra, X. Zhang, J. Tong, H. Hu, F. Luo, R. Kowalski, and J. Mazer, “MCDefender: Toward effective cyberbullying defense in mobile online social networks,” IWSPA 2017 - Proceedings of the 3rd ACM International Workshop on Security and Privacy Analytics, co-located with CODASPY 2017, pp. 37–42, 2017.

[22]

N. V. Chawla, N. Japkowicz, and P. Drive, “Editorial : Special Issue on Learning from Imbalanced Data Sets,” ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 1–6, 2004.

Digital Library

[23]

N. V. Chawla, “Data Mining for Imbalanced Datasets: An Overview,” in Data Mining and Knowledge Discovery Handbook, pp. 875–886, Boston, MA: Springer US, 2009.

[24]

N. Homem and J. P. Carvalho, “Authorship identification and author fuzzy ”fingerprints"," Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, pp. 180–185, 2011.

[25]

H. Rosa, F. Batista, and J. P. Carvalho, “Twitter Topic Fuzzy Fingerprints,” in 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 776–783, IEEE, jul 2014.

[26]

H. Rosa, J. Carvalho, and F. Batista, “Detecting a tweet’s topic within a large number of portuguese twitter trends,” in OpenAccess Series in Informatics, vol. 38, 2014.

[27]

H. Rosa, Topic Detection within Public Social Networks. Master thesis, Instituto Superior Técnico - Universidade de Lisboa, 2014.

[28]

S. Curto, J. P. Carvalho, C. Salgado, S. M. Vieira, and J. M. C. Sousa, “Predicting ICU readmissions based on bedside medical text notes,” in 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 2144-a–2151-h, IEEE, jul 2016.

[29]

A. Carvalho, P. Calado, and J. P. Carvalho, “Combining ratings and item descriptions in recommendation systems using fuzzy fingerprints,” in 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6, IEEE, jul 2017.

Cited By

Korkmaz ERiaz MDeveci MKadry S(2024)A novel approach to fuzzy N-soft sets and its application for identifying and sanctioning cyber harassment on social media platformsArtificial Intelligence Review10.1007/s10462-023-10640-y57:1Online publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1007/s10462-023-10640-y
Maity KJha PSaha SBhattacharyya PAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)A Multitask Framework for Sentiment, Emotion and Sarcasm aware Cyberbullying Detection from Multi-modal Code-Mixed MemesProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531925(1739-1749)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531925
Qiu JMoh MMoh TOgden CGamess E(2022)Multi-modal detection of cyberbullying on TwitterProceedings of the 2022 ACM Southeast Conference10.1145/3476883.3520222(9-16)Online publication date: 18-Apr-2022
https://dl.acm.org/doi/10.1145/3476883.3520222

Index Terms

Using Fuzzy Fingerprints for Cyberbullying Detection in Social Networks
1. Applied computing

Index terms have been assigned to the content through auto-classification.

Recommendations

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 ...
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 (...
Intersectionality and cyberbullying

Display Omitted Our paper applies an intersectional approach to the study of cyberbullying.We explore the conditional impact of race, gender, and sexuality on victimization.We conducted an original survey of students in a Midwestern high school (N=752)...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Jul 2018

1786 pages

Copyright © 2018.

Publisher

IEEE Press

Publication History

Published: 08 July 2018

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

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

Reflects downloads up to 11 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Korkmaz ERiaz MDeveci MKadry S(2024)A novel approach to fuzzy N-soft sets and its application for identifying and sanctioning cyber harassment on social media platformsArtificial Intelligence Review10.1007/s10462-023-10640-y57:1Online publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1007/s10462-023-10640-y
Maity KJha PSaha SBhattacharyya PAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)A Multitask Framework for Sentiment, Emotion and Sarcasm aware Cyberbullying Detection from Multi-modal Code-Mixed MemesProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531925(1739-1749)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531925
Qiu JMoh MMoh TOgden CGamess E(2022)Multi-modal detection of cyberbullying on TwitterProceedings of the 2022 ACM Southeast Conference10.1145/3476883.3520222(9-16)Online publication date: 18-Apr-2022
https://dl.acm.org/doi/10.1145/3476883.3520222

View Options

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

Media

Figures

Other

Tables

View Table of Contents