Hate Content Detection via Novel Pre-Processing Sequencing and Ensemble Methods
<|reference_start|>Hate Content Detection via Novel Pre-Processing Sequencing and Ensemble Methods: Social media, particularly Twitter, has seen a significant increase in incidents like trolling and hate speech. Thus, identifying hate speech is the need of the hour. This paper introduces a computational framework to curb the hate content on the web. Specifically, this study presents an exhaustive study of pre-processing approaches by studying the impact of changing the sequence of text pre-processing operations for the identification of hate content. The best-performing pre-processing sequence, when implemented with popular classification approaches like Support Vector Machine, Random Forest, Decision Tree, Logistic Regression and K-Neighbor provides a considerable boost in performance. Additionally, the best pre-processing sequence is used in conjunction with different ensemble methods, such as bagging, boosting and stacking to improve the performance further. Three publicly available benchmark datasets (WZ-LS, DT, and FOUNTA), were used to evaluate the proposed approach for hate speech identification. The proposed approach achieves a maximum accuracy of 95.14% highlighting the effectiveness of the unique pre-processing approach along with an ensemble classifier.<|reference_end|>
@article{chhabra2024hate,
    title={Hate Content Detection via Novel Pre-Processing Sequencing and Ensemble
  Methods},
    author={Anusha Chhabra, Dinesh Kumar Vishwakarma},
    journal={arXiv preprint arXiv:2409.05134},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05134},
    primaryClass={cs.CL}
}