Comparative performance of ensemble machine learning for Arabic cyberbullying and offensive language detection.
Since cyberbullying impacts both individual victims and entire society, research on abusive language and its detection has attracted attention in recent years. Because social media sites like Facebook, Instagram, Twitter, and others are so widely accessible, hate speech, bullying, sexism, racism, ag...
| Publicado en: | Language Resources & Evaluation Vol. 58; no. 2; pp. 695 - 713 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Jun2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=178064684&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 178064684 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2024 vid: 58 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 178064684 10.1007/s10579-023-09683-y ppf: 695 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1MB tig: atl: Comparative performance of ensemble machine learning for Arabic cyberbullying and offensive language detection. aug: au: Khairy, Marwa Mahmoud, Tarek M. Omar, Ahmed Abd El-Hafeez, Tarek affil: https://ror.org/02hcv4z63 Faculty of Computers and Information, Minia University, El Minya, Egypt https://ror.org/05p2q6194 Faculty of Computers and Artificial Intelligence, University of Sadat City, Sadat City, Egypt https://ror.org/02hcv4z63 Computer Science Department, Faculty of Science, Minia University, El Minya, Egypt Deraya University, El Minya, Egypt su: Cyberbullying Machine learning Social media Online social networks Machine performance Invective sug: subj: Cyberbullying Machine learning Social media Online social networks Machine performance Invective keyword: Arabic offensive language Ensemble machine learning NLP OSN ab: Since cyberbullying impacts both individual victims and entire society, research on abusive language and its detection has attracted attention in recent years. Because social media sites like Facebook, Instagram, Twitter, and others are so widely accessible, hate speech, bullying, sexism, racism, aggressive material, harassment, poisonous comments, and other types of abuse have all substantially increased. Due to the critical requirement to detect, regulate, and limit the spread of harmful content on social networking sites, we conducted this study to automate the detection of offensive language or cyberbullying. We created a new Arabic balanced data set to be used in the offensive detection process because having a balanced data set for a model would result in improved accuracy models. Recently, the performance of single classifiers has been improved using ensemble machine learning. The purpose of this study is to examine the effectiveness of several single and ensemble machine learning algorithms in identifying Arabic text that contains foul language and cyberbullying. Applying them to three Arabic datasets, we have selected three machine learning classifiers and three ensemble models for this aim. Two of them are offensive datasets that are readily accessible in the public, while the third one was created. The results showed that the single learner machine learning strategy is inferior to the ensemble machine learning methodology. Voting performs is the best performing trained ensemble machine learning classifier, outperforming the best single learner classifier (65.1%, 76.2%, and 98%) for the same datasets with accuracy scores of (71.1%, 76.7%, and 98.5%) for each of the three datasets used. Finally, we improve the voting technique's performance through hyperparameter tuning on the Arabic cyberbullying data set. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2024. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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