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24725 Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies
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Dadvar, M. and Trieschnigg, R.B. and de Jong, F.M.G. (2014) Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies. In: Proceedings of the 27th Canadian Conference on Artificial Intelligence, Canadian AI 2014, 6-9 May 2014, Montreal, Canada. pp. 275-281. Lecture Notes in Computer Science 8436. Springer Verlag. ISSN 0302-9743 ISBN 978-3-319-06482-6

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Official URL: http://dx.doi.org/10.1007/978-3-319-06483-3_25

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Abstract

Cyberbullying is becoming a major concern in online environments with troubling consequences. However, most of the technical studies have focused on the detection of cyberbullying through identifying harassing comments rather than preventing the incidents by detecting the bullies. In this work we study the automatic detection of bully users on YouTube. We compare three types of automatic detection: an expert system, supervised machine learning models, and a hybrid type combining the two. All these systems assign a score indicating the level of “bulliness” of online bullies. We demonstrate that the expert system outperforms the machine learning models. The hybrid classifier shows an even better performance.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-HMI: Human Media Interaction
Research Program:CTIT-NICE: Natural Interaction in Computer-mediated Environments
Research Project:Puppy-IR: An Open Source Environment to construct Information Services for Children
Uncontrolled Keywords:Expert system, Identity identification, bulliness score, cyberbullying
ID Code:24725
Status:Published
Deposited On:28 July 2014
Refereed:Yes
International:Yes
More Information:statisticsmetis

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