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공공누리This item is licensed Korea Open Government License

dc.contributor.author
Ali, Wan Noor Hamiza Wan
dc.contributor.author
Mohd, Masnizah
dc.contributor.author
Fauzi, Fariza
dc.date.accessioned
2022-02-17T07:20:23Z
dc.date.available
2022-02-17T07:20:23Z
dc.date.issued
2021-03-30
dc.identifier.issn
2287-4577
dc.identifier.uri
https://repository.kisti.re.kr/handle/10580/16263
dc.description.abstract
The popularity of social networking sites (SNS) has facilitated communication between users. The usage of SNS helps users in their daily life in various ways such as sharing of opinions, keeping in touch with old friends, making new friends, and getting information. However, some users misuse SNS to belittle or hurt others using profanities, which is typical in cyberbullying incidents. Thus, in this study, we aim to identify profane words from the ASKfm corpus to analyze the profane word distribution across four different roles involved in cyberbullying based on lexicon dictionary. These four roles are: harasser, victim, bystander that assists the bully, and bystander that defends the victim. Evaluation in this study focused on occurrences of the profane word for each role from the corpus. The top 10 common words used in the corpus are also identified and represented in a graph. Results from the analysis show that these four roles used profane words in their conversation with different weightage and distribution, even though the profane words used are mostly similar. The harasser is the first ranked that used profane words in the conversation compared to other roles. The results can be further explored and considered as a potential feature in a cyberbullying detection model using a machine learning approach. Results in this work will contribute to formulate the suitable representation. It is also useful in modeling a cyberbullying detection model based on the identification of profane word distribution across different cyberbullying roles in social networks for future works.
dc.format
text/plain; charset=utf-8
dc.language.iso
eng
dc.publisher
Korea Institute of Science and Technology Information
dc.relation.ispartofseries
Journal of Information Science Theory and Practice;Volume 9 Issue 1
dc.title
Identification of Profane Words in Cyberbullying Incidents within Social Networks
dc.type
Serial
dc.identifier.doi
https://doi.org/10.1633/JISTaP.2021.9.1.2
dc.contributor.approver
KOAR, ADMIN
dc.date.dateaccepted
2022-02-17T07:20:23Z
dc.date.datesubmitted
2022-02-17T07:20:23Z
dc.subject.keyword
cyberbullying
dc.subject.keyword
profane words
dc.subject.keyword
cybercrime
dc.subject.keyword
harassment
dc.subject.keyword
social network
dc.subject.keyword
machine learning
Appears in Collections:
8. KISTI 간행물 > JISTaP > Vol. 9 - No. 1
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