Citation :

I. Imthiyas Banu, Velumani Thiyagarajan, "Nonlinear Seagull Optimized Bidrectional Recurrent Network for English Hate Speech Detection in Online Social Network," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 1, pp. 25-38, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I1P102

Abstract

Online Social Network (OSN) provides services or sites to facilitate social interaction for identifying people’s general attention, discussion, and exchanging information. The Proposed Nonlinear Evolutionary Seagull Optimized Bidirectional Gated Neural Network (NESO-BGNN) is introduced for hate speech detection in English in OSN. It comprises preprocessing, keyword extraction, and classification. First, Robust Scaling Normalization-based preprocessing is applied to handle outliers. Second, Nonlinear Evolutionary-based Seagull Optimization algorithm extracts optimal keywords for hate speech detection. Finally, the Bidirectional Gated Recurrent Neural Network (BGRNN) detects hate speech accurately with a lower misclassification rate. An experiment was carried out using the Hate Speech and Offensive Language Dataset with different factors.

Keywords

Robust Scaling Normalization, Inter Quartile Range, Nonlinear Evolutionary, Seagull Optimization, Bidirectional Gated Recurrent Neural Network.

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