Behaviour Analysis Among Adolescents And Children For Cyberbullying Based On Twitter And Kaggle Dataset
Nowadays, the online platform is primarily used to communicate and connect with people. It controlled many lives based on many aspects. When online, not all users prefer what they see. They can speak up without thinking about the effect on someone. From this event, it may lead to cyberbullying activ...
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| Format: | Undergraduates Project Papers |
| Language: | en |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/40196/1/CA19084.pdf http://umpir.ump.edu.my/id/eprint/40196/ |
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| _version_ | 1831530026362732544 |
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| author | Afiefah Hannani, Abdul Halim |
| author_facet | Afiefah Hannani, Abdul Halim |
| author_sort | Afiefah Hannani, Abdul Halim |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Nowadays, the online platform is primarily used to communicate and connect with people. It controlled many lives based on many aspects. When online, not all users prefer what they see. They can speak up without thinking about the effect on someone. From this event, it may lead to cyberbullying activity since all the users are free to throw their opinion on social media without hesitation. Not only that, but based on online judgment, it can also affect someone’s mental health. Therefore, cyberbullying detection using a Machine Learning approach is suggested. This study presents the comparison of three Machine Learning algorithms for the detection using cyberbullying activity on social media platforms specifically Twitter. The dataset to perform the algorithm will be retrieved from an open-source website called Kaggle where it will be used for the training and testing process. The algorithms include Support Vector Machine (SVM), Naïve Bayes, and Decision Tree. The purpose of this study is to see the accuracy of the algorithms and compare it. The highest algorithm will be chosen as the best model and algorithm that can be used to detect cyberbullying tweet text. |
| format | Undergraduates Project Papers |
| id | my.ump.umpir.40196 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2023 |
| record_format | eprints |
| spelling | my.ump.umpir.401962024-02-07T04:19:52Z http://umpir.ump.edu.my/id/eprint/40196/ Behaviour Analysis Among Adolescents And Children For Cyberbullying Based On Twitter And Kaggle Dataset Afiefah Hannani, Abdul Halim QA75 Electronic computers. Computer science Nowadays, the online platform is primarily used to communicate and connect with people. It controlled many lives based on many aspects. When online, not all users prefer what they see. They can speak up without thinking about the effect on someone. From this event, it may lead to cyberbullying activity since all the users are free to throw their opinion on social media without hesitation. Not only that, but based on online judgment, it can also affect someone’s mental health. Therefore, cyberbullying detection using a Machine Learning approach is suggested. This study presents the comparison of three Machine Learning algorithms for the detection using cyberbullying activity on social media platforms specifically Twitter. The dataset to perform the algorithm will be retrieved from an open-source website called Kaggle where it will be used for the training and testing process. The algorithms include Support Vector Machine (SVM), Naïve Bayes, and Decision Tree. The purpose of this study is to see the accuracy of the algorithms and compare it. The highest algorithm will be chosen as the best model and algorithm that can be used to detect cyberbullying tweet text. 2023-02 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40196/1/CA19084.pdf Afiefah Hannani, Abdul Halim (2023) Behaviour Analysis Among Adolescents And Children For Cyberbullying Based On Twitter And Kaggle Dataset. Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah. |
| spellingShingle | QA75 Electronic computers. Computer science Afiefah Hannani, Abdul Halim Behaviour Analysis Among Adolescents And Children For Cyberbullying Based On Twitter And Kaggle Dataset |
| title | Behaviour Analysis Among Adolescents And Children For Cyberbullying Based On Twitter And Kaggle Dataset |
| title_full | Behaviour Analysis Among Adolescents And Children For Cyberbullying Based On Twitter And Kaggle Dataset |
| title_fullStr | Behaviour Analysis Among Adolescents And Children For Cyberbullying Based On Twitter And Kaggle Dataset |
| title_full_unstemmed | Behaviour Analysis Among Adolescents And Children For Cyberbullying Based On Twitter And Kaggle Dataset |
| title_short | Behaviour Analysis Among Adolescents And Children For Cyberbullying Based On Twitter And Kaggle Dataset |
| title_sort | behaviour analysis among adolescents and children for cyberbullying based on twitter and kaggle dataset |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/40196/1/CA19084.pdf http://umpir.ump.edu.my/id/eprint/40196/ |
| url_provider | http://umpir.ump.edu.my/ |