Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies

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 st...

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Published inAdvances in Artificial Intelligence pp. 275 - 281
Main Authors Dadvar, Maral, Trieschnigg, Dolf, de Jong, Franciska
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2014
SeriesLecture Notes in Computer Science
Subjects
Bully User
Expert System
Machine Learning Model
Person Pronoun
Support Vector Machine
Online AccessGet full text
ISBN9783319064826
3319064827
ISSN0302-9743
1611-3349
1611-3349
DOI10.1007/978-3-319-06483-3_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.
AbstractList 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.
Author de Jong, Franciska
Dadvar, Maral
Trieschnigg, Dolf
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Snippet Cyberbullying is becoming a major concern in online environments with troubling consequences. However, most of the technical studies have focused on the...
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StartPage 275
SubjectTerms Bully User
Expert System
Machine Learning Model
Person Pronoun
Support Vector Machine
Title Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies
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