Using machine learning for automated role identification in cyberbullying

Bullying has been an old problem that experts believe does not cease to exist as people grow older (Krantz, 2012). With the advent of computer technology, bullying has also evolved from being a physical experience to a virtual experience, now widely known as cyberbullying. Since traditional bullying...

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Main Author: Ng, Louie Anson
Format: text
Language:English
Published: Animo Repository 2014
Online Access:https://animorepository.dlsu.edu.ph/etd_masteral/4702
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Institution: De La Salle University
Language: English
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spelling oai:animorepository.dlsu.edu.ph:etd_masteral-115402021-02-01T04:01:50Z Using machine learning for automated role identification in cyberbullying Ng, Louie Anson Bullying has been an old problem that experts believe does not cease to exist as people grow older (Krantz, 2012). With the advent of computer technology, bullying has also evolved from being a physical experience to a virtual experience, now widely known as cyberbullying. Since traditional bullying involves the participation of different roles, the proponent speculates that the same roles are also present in cyberbullying. Existing researches included determining texts which contained cyberbullying, while a few involved the use of roles in determining whether bullying occured or not. The problem is that these models are created for classifying texts written in English, and thus cannot be used in the local context. By using social media sites as sources for model training, the research created a support vector machine (SVM) based model for detecting bullying through the use of roles by using word features which were selected using the TF-IDF algorithm combined with the use of weights. 10-fold cross validation showed an accuracy of 59.7% for using 171 unique word features with a Kappa statistic of only 42.3%. 2014-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_masteral/4702 Master's Theses English Animo Repository
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
description Bullying has been an old problem that experts believe does not cease to exist as people grow older (Krantz, 2012). With the advent of computer technology, bullying has also evolved from being a physical experience to a virtual experience, now widely known as cyberbullying. Since traditional bullying involves the participation of different roles, the proponent speculates that the same roles are also present in cyberbullying. Existing researches included determining texts which contained cyberbullying, while a few involved the use of roles in determining whether bullying occured or not. The problem is that these models are created for classifying texts written in English, and thus cannot be used in the local context. By using social media sites as sources for model training, the research created a support vector machine (SVM) based model for detecting bullying through the use of roles by using word features which were selected using the TF-IDF algorithm combined with the use of weights. 10-fold cross validation showed an accuracy of 59.7% for using 171 unique word features with a Kappa statistic of only 42.3%.
format text
author Ng, Louie Anson
spellingShingle Ng, Louie Anson
Using machine learning for automated role identification in cyberbullying
author_facet Ng, Louie Anson
author_sort Ng, Louie Anson
title Using machine learning for automated role identification in cyberbullying
title_short Using machine learning for automated role identification in cyberbullying
title_full Using machine learning for automated role identification in cyberbullying
title_fullStr Using machine learning for automated role identification in cyberbullying
title_full_unstemmed Using machine learning for automated role identification in cyberbullying
title_sort using machine learning for automated role identification in cyberbullying
publisher Animo Repository
publishDate 2014
url https://animorepository.dlsu.edu.ph/etd_masteral/4702
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