demonstration

4Is of social bully filtering: identity, inference, influence, and intervention

Authors:

Yunfei Chen,

Lanbo Zhang,

Aaron Michelony,

Yi ZhangAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 2677 - 2679

https://doi.org/10.1145/2396761.2398723

Published: 29 October 2012 Publication History

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Abstract

As the increasing of popularity of social web, cyber bullying has become a more and more serious issue among children. Bullying causes huge negative effects on children, even suicide. SocialFilter is a realtime system that helps parents and educators track children's messages on Twitter, especially in order to detect whether they have been bullied or bullying others. The aim of the system is 4 I's, identity of bullies, inference of bullying message, influence of bully behavior, and intervention. We solve this problem by using machine learning technique. The current system is tracking tens of thousands of active children users on Twitter and automatically detect bullying messages at real time.

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Cited By

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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
Tomkins SGetoor LChen YZhang Y(2018)A socio-linguistic model for cyberbullying detectionProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3382225.3382237(53-60)Online publication date: 28-Aug-2018
https://dl.acm.org/doi/10.5555/3382225.3382237
Tomkins SGetoor LChen YZhang Y(2018)A Socio-linguistic Model for Cyberbullying Detection2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM.2018.8508294(53-60)Online publication date: Aug-2018
https://doi.org/10.1109/ASONAM.2018.8508294
Show More Cited By

Index Terms

4Is of social bully filtering: identity, inference, influence, and intervention
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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CIKM'12

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all

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
Tomkins SGetoor LChen YZhang Y(2018)A socio-linguistic model for cyberbullying detectionProceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3382225.3382237(53-60)Online publication date: 28-Aug-2018
https://dl.acm.org/doi/10.5555/3382225.3382237
Tomkins SGetoor LChen YZhang Y(2018)A Socio-linguistic Model for Cyberbullying Detection2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM.2018.8508294(53-60)Online publication date: Aug-2018
https://doi.org/10.1109/ASONAM.2018.8508294
Jia HWisniewski PXu HRosson MCarroll JCosley DForte ACiolfi LMcDonald D(2015)Risk-taking as a Learning Process for Shaping Teen's Online Information Privacy BehaviorsProceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing10.1145/2675133.2675287(583-599)Online publication date: 28-Feb-2015
https://dl.acm.org/doi/10.1145/2675133.2675287

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Examining middle school students' views on text bullying

Cyberbullying: Eliciting harm without consequence

Buddy: Helping School-age Children to Combat Bullying