| Title | Cyberbullying Predictive Model: Implementation of Machine Learning Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ali, Wan Noor Hamiza Wan, Mohd, Masnizah, Fauzi, Fariza |
| Conference Name | 2021 Fifth International Conference on Information Retrieval and Knowledge Management (CAMP) |
| Keywords | cyberbullying, Decision Tree, feature selection, hyperparameter, machine learning, machine learning algorithms, Measurement, Metrics, Predictive models, predictive security metrics, pubcrawl, social networking (online), Static VAr compensators, Support Vector Classification Linear, support vector machine classification |
| Abstract | Machine learning is implemented extensively in various applications. The machine learning algorithms teach computers to do what comes naturally to humans. The objective of this study is to do comparison on the predictive models in cyberbullying detection between the basic machine learning system and the proposed system with the involvement of feature selection technique, resampling and hyperparameter optimization by using two classifiers; Support Vector Classification Linear and Decision Tree. Corpus from ASKfm used to extract word n-grams features before implemented into eight different experiments setup. Evaluation on performance metric shows that Decision Tree gives the best performance when tested using feature selection without resampling and hyperparameter optimization involvement. This shows that the proposed system is better than the basic setting in machine learning. |
| DOI | 10.1109/CAMP51653.2021.9497932 |
| Citation Key | ali_cyberbullying_2021 |
Cyberbullying Predictive Model: Implementation of Machine Learning Approach