Cyberbullying-Related Automated Hate Speech Detection on Social Media Platforms Using Stack Ensemble Classification Method
Abstract Hate speech (HS) has grown because of increasing social media platform usage, which includes Twitter, YouTube, and Facebook. The frequent attempts to implement automated detection systems remain unsuccessful at separating hate speech from objectionable language, because user-generated conte...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-025-00919-z |
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| Summary: | Abstract Hate speech (HS) has grown because of increasing social media platform usage, which includes Twitter, YouTube, and Facebook. The frequent attempts to implement automated detection systems remain unsuccessful at separating hate speech from objectionable language, because user-generated content tends toward informal, brief, and diverse expressions. The determination of hate speech within texts proves exceptionally hard, since precise context detection is needed to distinguish abusive language from neutral statements. Precision in hate speech identification and filtering stands essential, because these online content forms have negative impacts on both minority and majority groups while heightening their conflicts. The research presents a stacked ensemble classification system that classifies tweets into three groups: hate speech, abusive language, or neutral. The framework uses term frequency–inverse document frequency (TF–IDF) extracted from tweet texts for which support vector machine (SVM), together with Random Forest, XGBoost, and Logistic Regression, base machine learning models function as classifiers. The final model outcome results from linking several base learning models into an ensemble configuration. The Kaggle Hate Speech data set served as training material for the system, because it contained 24,784 tweets along with eight attributes. The model performance received improvement through exclusion of manually derived features. The proposed ensemble model demonstrated superior performance with 96% accuracy, while each single classifier had lower accuracy rates (SVM: 93%, Random Forest: 94%, and XGBoost: 88%). The research outcomes show stacking represents an effective method to enhance systems for detecting hate speech operating on social media platforms. |
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| ISSN: | 1875-6883 |