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A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
| Content Provider | MDPI |
|---|---|
| Author | Muneer, Amgad Fati, Suliman Mohamed |
| Copyright Year | 2020 |
| Description | The advent of social media, particularly Twitter, raises many issues due to a misunderstanding regarding the concept of freedom of speech. One of these issues is cyberbullying, which is a critical global issue that affects both individual victims and societies. Many attempts have been introduced in the literature to intervene in, prevent, or mitigate cyberbullying; however, because these attempts rely on the victims’ interactions, they are not practical. Therefore, detection of cyberbullying without the involvement of the victims is necessary. In this study, we attempted to explore this issue by compiling a global dataset of 37,373 unique tweets from Twitter. Moreover, seven machine learning classifiers were used, namely, Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), Stochastic Gradient Descent (SGD), Random Forest (RF), AdaBoost (ADB), Naive Bayes (NB), and Support Vector Machine (SVM). Each of these algorithms was evaluated using accuracy, precision, recall, and F1 score as the performance metrics to determine the classifiers’ recognition rates applied to the global dataset. The experimental results show the superiority of LR, which achieved a median accuracy of around 90.57%. Among the classifiers, logistic regression achieved the best F1 score (0.928), SGD achieved the best precision (0.968), and SVM achieved the best recall (1.00). |
| Starting Page | 187 |
| e-ISSN | 19995903 |
| DOI | 10.3390/fi12110187 |
| Journal | Future Internet |
| Issue Number | 11 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-10-29 |
| Access Restriction | Open |
| Subject Keyword | Future Internet Information and Library Science Medical Informatics Cyberbullying Detection Tweets Classification Twitter Logistic Regression Random Forest Light Gbm Sgd Adaboost Naive Bayes Svm |
| Content Type | Text |
| Resource Type | Article |
