Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network

The popularity of online social networks has created massive social communication among their users and this leads to a huge amount of user-generated communication data. In recent years, Cyberbullying has grown into a major problem with the growth of online communication and social media. Cyberbully...

Full description

Saved in:
Bibliographic Details
Main Authors: Al-Garadi, M.A., Varathan, K.D., Ravana, S.D.
Format: Article
Published: Elsevier 2016
Subjects:
Online Access:http://eprints.um.edu.my/18213/
https://doi.org/10.1016/j.chb.2016.05.051
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.um.eprints.18213
record_format eprints
spelling my.um.eprints.182132017-11-10T03:40:47Z http://eprints.um.edu.my/18213/ Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network Al-Garadi, M.A. Varathan, K.D. Ravana, S.D. QA75 Electronic computers. Computer science The popularity of online social networks has created massive social communication among their users and this leads to a huge amount of user-generated communication data. In recent years, Cyberbullying has grown into a major problem with the growth of online communication and social media. Cyberbullying has been recognized recently as a serious national health issue among online social network users and developing an efficient detection model holds tremendous practical significance. In this paper, we have proposed set of unique features derived from Twitter; network, activity, user, and tweet content, based on these feature, we developed a supervised machine learning solution for detecting cyberbullying in the Twitter. An evaluation demonstrates that our developed detection model based on our proposed features, achieved results with an area under the receiver-operating characteristic curve of 0.943 and an f-measure of 0.936. These results indicate that the proposed model based on these features provides a feasible solution to detecting Cyberbullying in online communication environments. Finally, we compare result obtained using our proposed features with the result obtained from two baseline features. The comparison outcomes show the significance of the proposed features. Elsevier 2016 Article PeerReviewed Al-Garadi, M.A. and Varathan, K.D. and Ravana, S.D. (2016) Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network. Computers in Human Behavior, 63. pp. 433-443. ISSN 0747-5632 https://doi.org/10.1016/j.chb.2016.05.051 doi:10.1016/j.chb.2016.05.051
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Al-Garadi, M.A.
Varathan, K.D.
Ravana, S.D.
Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network
description The popularity of online social networks has created massive social communication among their users and this leads to a huge amount of user-generated communication data. In recent years, Cyberbullying has grown into a major problem with the growth of online communication and social media. Cyberbullying has been recognized recently as a serious national health issue among online social network users and developing an efficient detection model holds tremendous practical significance. In this paper, we have proposed set of unique features derived from Twitter; network, activity, user, and tweet content, based on these feature, we developed a supervised machine learning solution for detecting cyberbullying in the Twitter. An evaluation demonstrates that our developed detection model based on our proposed features, achieved results with an area under the receiver-operating characteristic curve of 0.943 and an f-measure of 0.936. These results indicate that the proposed model based on these features provides a feasible solution to detecting Cyberbullying in online communication environments. Finally, we compare result obtained using our proposed features with the result obtained from two baseline features. The comparison outcomes show the significance of the proposed features.
format Article
author Al-Garadi, M.A.
Varathan, K.D.
Ravana, S.D.
author_facet Al-Garadi, M.A.
Varathan, K.D.
Ravana, S.D.
author_sort Al-Garadi, M.A.
title Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network
title_short Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network
title_full Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network
title_fullStr Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network
title_full_unstemmed Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network
title_sort cybercrime detection in online communications: the experimental case of cyberbullying detection in the twitter network
publisher Elsevier
publishDate 2016
url http://eprints.um.edu.my/18213/
https://doi.org/10.1016/j.chb.2016.05.051
_version_ 1643690642212126720
score 13.211869