RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features
Online platforms are fostering social interaction, but unfortunately, this has given rise to antisocial behaviors such as cyberbullying, trolling, and hate speech on a global scale. The detection of hate and aggression has become a vital aspect of combating cyberbullying and cyberharassment. Cyberbu...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10495045/ |
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| author | Arwa A. Jamjoom Hanen Karamti Muhammad Umer Shtwai Alsubai Tai-Hoon Kim Imran Ashraf |
| author_facet | Arwa A. Jamjoom Hanen Karamti Muhammad Umer Shtwai Alsubai Tai-Hoon Kim Imran Ashraf |
| author_sort | Arwa A. Jamjoom |
| collection | DOAJ |
| description | Online platforms are fostering social interaction, but unfortunately, this has given rise to antisocial behaviors such as cyberbullying, trolling, and hate speech on a global scale. The detection of hate and aggression has become a vital aspect of combating cyberbullying and cyberharassment. Cyberbullying involves using aggressive and offensive language including rude, insulting, hateful, and teasing comments to harm individuals on social media platforms. Human moderation is both slow and expensive, making it impractical in the face of rapidly growing data. Automatic detection systems are essential to curb trolling effectively. This research deals with the challenge of automatically identifying cyberbullying in tweets from a publicly available cyberbullying dataset. This research work employs robustly optimized bidirectional encoder representations from the transformers approach (RoBERTa), utilizing global vectors for word representation (GloVe) word embedding features. The proposed approach is further compared with the state-of-the-art machine, deep, and transformer-based learning approaches with the FastText word embedding approach. Statistical results demonstrate that the proposed model outperforms others, achieving a 95% accuracy for detecting cyberbullying tweets. In addition, the model obtains 95%, 97%, and 96% for precision, recall, and F1 score, respectively. Results from k-fold cross-validation further affirm the supremacy of the proposed model with a mean accuracy of 95.07%. |
| format | Article |
| id | doaj-art-ecc0a4bc13184eadabece7ce528b4777 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ecc0a4bc13184eadabece7ce528b47772025-08-20T03:42:15ZengIEEEIEEE Access2169-35362024-01-0112589505895910.1109/ACCESS.2024.338663710495045RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe FeaturesArwa A. Jamjoom0Hanen Karamti1Muhammad Umer2Shtwai Alsubai3https://orcid.org/0000-0002-6584-7400Tai-Hoon Kim4https://orcid.org/0000-0003-0117-8102Imran Ashraf5https://orcid.org/0009-0002-4598-1482Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi ArabiaDepartment of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, PakistanDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaSchool of Electrical and Computer Engineering, Yeosu Campus, Chonnam National University, Yeosu-si, Jeollanam-do, Republic of KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of KoreaOnline platforms are fostering social interaction, but unfortunately, this has given rise to antisocial behaviors such as cyberbullying, trolling, and hate speech on a global scale. The detection of hate and aggression has become a vital aspect of combating cyberbullying and cyberharassment. Cyberbullying involves using aggressive and offensive language including rude, insulting, hateful, and teasing comments to harm individuals on social media platforms. Human moderation is both slow and expensive, making it impractical in the face of rapidly growing data. Automatic detection systems are essential to curb trolling effectively. This research deals with the challenge of automatically identifying cyberbullying in tweets from a publicly available cyberbullying dataset. This research work employs robustly optimized bidirectional encoder representations from the transformers approach (RoBERTa), utilizing global vectors for word representation (GloVe) word embedding features. The proposed approach is further compared with the state-of-the-art machine, deep, and transformer-based learning approaches with the FastText word embedding approach. Statistical results demonstrate that the proposed model outperforms others, achieving a 95% accuracy for detecting cyberbullying tweets. In addition, the model obtains 95%, 97%, and 96% for precision, recall, and F1 score, respectively. Results from k-fold cross-validation further affirm the supremacy of the proposed model with a mean accuracy of 95.07%.https://ieeexplore.ieee.org/document/10495045/CyberbullyingRoBERTaGloVeFastTexttransformer based learning |
| spellingShingle | Arwa A. Jamjoom Hanen Karamti Muhammad Umer Shtwai Alsubai Tai-Hoon Kim Imran Ashraf RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features IEEE Access Cyberbullying RoBERTa GloVe FastText transformer based learning |
| title | RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features |
| title_full | RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features |
| title_fullStr | RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features |
| title_full_unstemmed | RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features |
| title_short | RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features |
| title_sort | robertanet enhanced roberta transformer based model for cyberbullying detection with glove features |
| topic | Cyberbullying RoBERTa GloVe FastText transformer based learning |
| url | https://ieeexplore.ieee.org/document/10495045/ |
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