Hate Content Detection via Novel Pre-Processing Sequencing and Ensemble Methods
This work addresses the problem of hate content detection for social media platforms, but it is incremental as it focuses on optimizing pre-processing sequences and ensemble techniques.
The paper tackled hate speech detection on social media by exploring the impact of different sequences of text pre-processing operations and combining them with ensemble methods, achieving a maximum accuracy of 95.14% on benchmark datasets.
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.