Hate Content Detection via Novel Pre-Processing Sequencing and Ensemble Methods

• URL: http://arxiv.org/abs/2409.05134v1
• Date: Sun, 8 Sep 2024 15:32:17 GMT
• Title: Hate Content Detection via Novel Pre-Processing Sequencing and Ensemble Methods
• Authors: Anusha Chhabra, Dinesh Kumar Vishwakarma,
• Abstract summary: Social media, particularly Twitter, has seen a significant increase in incidents like trolling and hate speech. This paper introduces a computational framework to curb the hate content on the web.
• Score: 15.647035299476894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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