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Cyberbully Detection Survey: Malay-English Glossary of Cyberbullying Incidents

This article presents the results of a survey conducted to elicit keywords or phrases relating to cyberbullying incidents in both English and Malay languages commonly used in Malaysian society. The keywords or phrases can be utilized as a Malay and English cyberbullying glossary in the development o...

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Main Authors:	Din M.M., Rahim F.A., Anwar R.M., Bakar A.A., Latif A.A.
Other Authors:	55348871200
Format:	Conference Paper
Published:	Springer Science and Business Media Deutschland GmbH 2024
Subjects:	Cyberbullying Cyberbullying detection Machine learning Text extraction Computer crime Glossaries Auto-detection Cyber bullying Cyberbully English languages Machine-learning Malay languages Malaysians
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id	my.uniten.dspace-34601
record_format	dspace
spelling	my.uniten.dspace-346012024-10-14T11:21:00Z Cyberbully Detection Survey: Malay-English Glossary of Cyberbullying Incidents Din M.M. Rahim F.A. Anwar R.M. Bakar A.A. Latif A.A. 55348871200 57350579500 24721188400 35178991300 46461488000 Cyberbullying Cyberbullying detection Machine learning Text extraction Computer crime Glossaries Auto-detection Cyber bullying Cyberbully Cyberbullying detection English languages Machine-learning Malay languages Malaysians Text extraction Machine learning This article presents the results of a survey conducted to elicit keywords or phrases relating to cyberbullying incidents in both English and Malay languages commonly used in Malaysian society. The keywords or phrases can be utilized as a Malay and English cyberbullying glossary in the development of an auto-detection cyberbullying tool. A set of questionnaires were distributed among 329 respondents ages 15�30�years in Malaysia in the form of an online survey over one-and-a-half-month starting 1 November 2021. This study was conducted to test the items� reliability using Cronbach�s alpha values. There are three (3) Sections to this questionnaire Sect.�1 is about the demographics of the respondents, Sect.�2 is related to Cyberbullying, and Sect.�3 shows a few scenarios that might be or might not be a cyberbullying incident. Findings from the analyses showed that 447 words were collected, and all of these were later grouped into five (5) categories 5 Intellectual, Physical Appearance, Insulting/Offensive, Intimidating and Others. Making offensive comments or doing insulting posts was the most cyberbullying form made by the bullies. Five (5) popular words or phrases were identified as the common cyberbullying content in Malaysian society. � 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Final 2024-10-14T03:20:59Z 2024-10-14T03:20:59Z 2023 Conference Paper 10.1007/978-981-19-8406-8_50 2-s2.0-85161362147 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161362147&doi=10.1007%2f978-981-19-8406-8_50&partnerID=40&md5=6aa7e1981767591a3a51bf262683794a https://irepository.uniten.edu.my/handle/123456789/34601 983 LNEE 645 657 Springer Science and Business Media Deutschland GmbH Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Cyberbullying Cyberbullying detection Machine learning Text extraction Computer crime Glossaries Auto-detection Cyber bullying Cyberbully Cyberbullying detection English languages Machine-learning Malay languages Malaysians Text extraction Machine learning
spellingShingle	Cyberbullying Cyberbullying detection Machine learning Text extraction Computer crime Glossaries Auto-detection Cyber bullying Cyberbully Cyberbullying detection English languages Machine-learning Malay languages Malaysians Text extraction Machine learning Din M.M. Rahim F.A. Anwar R.M. Bakar A.A. Latif A.A. Cyberbully Detection Survey: Malay-English Glossary of Cyberbullying Incidents
description	This article presents the results of a survey conducted to elicit keywords or phrases relating to cyberbullying incidents in both English and Malay languages commonly used in Malaysian society. The keywords or phrases can be utilized as a Malay and English cyberbullying glossary in the development of an auto-detection cyberbullying tool. A set of questionnaires were distributed among 329 respondents ages 15�30�years in Malaysia in the form of an online survey over one-and-a-half-month starting 1 November 2021. This study was conducted to test the items� reliability using Cronbach�s alpha values. There are three (3) Sections to this questionnaire
author2	55348871200
author_facet	55348871200 Din M.M. Rahim F.A. Anwar R.M. Bakar A.A. Latif A.A.
format	Conference Paper
author	Din M.M. Rahim F.A. Anwar R.M. Bakar A.A. Latif A.A.
author_sort	Din M.M.
title	Cyberbully Detection Survey: Malay-English Glossary of Cyberbullying Incidents
title_short	Cyberbully Detection Survey: Malay-English Glossary of Cyberbullying Incidents
title_full	Cyberbully Detection Survey: Malay-English Glossary of Cyberbullying Incidents
title_fullStr	Cyberbully Detection Survey: Malay-English Glossary of Cyberbullying Incidents
title_full_unstemmed	Cyberbully Detection Survey: Malay-English Glossary of Cyberbullying Incidents
title_sort	cyberbully detection survey: malay-english glossary of cyberbullying incidents
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2024
_version_	1814061187386572800
score	13.222552

Cyberbully Detection Survey: Malay-English Glossary of Cyberbullying Incidents

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