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Cyberbully Detection Survey: Malay-English Glossary of Cyberbullying Incidents

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dc.citedby0
dc.contributor.authorDin M.M.en_US
dc.contributor.authorRahim F.A.en_US
dc.contributor.authorAnwar R.M.en_US
dc.contributor.authorBakar A.A.en_US
dc.contributor.authorLatif A.A.en_US
dc.contributor.authorid55348871200en_US
dc.contributor.authorid57350579500en_US
dc.contributor.authorid24721188400en_US
dc.contributor.authorid35178991300en_US
dc.contributor.authorid46461488000en_US
dc.date.accessioned2024-10-14T03:20:59Z
dc.date.available2024-10-14T03:20:59Z
dc.date.issued2023
dc.description.abstractThis 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 questionnaireen_US
dc.description.abstractSect.�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 5en_US
dc.description.abstractIntellectual, 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-981-19-8406-8_50
dc.identifier.epage657
dc.identifier.scopus2-s2.0-85161362147
dc.identifier.spage645
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85161362147&doi=10.1007%2f978-981-19-8406-8_50&partnerID=40&md5=6aa7e1981767591a3a51bf262683794a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34601
dc.identifier.volume983 LNEE
dc.pagecount12
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Electrical Engineering
dc.subjectCyberbullying
dc.subjectCyberbullying detection
dc.subjectMachine learning
dc.subjectText extraction
dc.subjectComputer crime
dc.subjectGlossaries
dc.subjectAuto-detection
dc.subjectCyber bullying
dc.subjectCyberbully
dc.subjectCyberbullying detection
dc.subjectEnglish languages
dc.subjectMachine-learning
dc.subjectMalay languages
dc.subjectMalaysians
dc.subjectText extraction
dc.subjectMachine learning
dc.titleCyberbully Detection Survey: Malay-English Glossary of Cyberbullying Incidentsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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