Evaluation of Neural Networks for the Identification of Hate Speech in Spanish Using Stylometric Features
DOI:
https://doi.org/10.46480/esj.10.2.295Keywords:
Artificial intelligence, Neural networks, Stylometry, Hate speech, Text classificationAbstract
Context: This research focuses on analyzing the effectiveness of neural networks in detecting hate speech in Spanish, using stylometric features and taking into account Ecuador's linguistic diversity. The main objective was to evaluate and analyze the accuracy of different neural network models in this specific text classification task. Method: Four neural network architectures were implemented and compared: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Transformer, applying the CRISP-DM methodology to structure the necessary tasks in each phase and their interrelationships. Results: The results revealed significant differences in the performance of the four models. The Transformer model stands out as the most efficient alternative, achieving a higher weighted F1 score (0.88) and an AUC of 0.92, indicating an optimal balance. Both CNN and RNN reached an overall accuracy of 87%, while the LSTM obtained the lowest accuracy at 86%. Conclusions: Consequently, the adoption of the Transformer model is recommended for automatic hate speech detection in Spanish texts, employing stylometric features and considering Ecuador's linguistic diversity. Furthermore, it is suggested to continue evaluating the model in a production environment to identify potential biases and make necessary adjustments. Future research includes incorporating texts in multiple languages and developing educational programs aimed at students, thereby fostering greater awareness of hate speech and its potential impact.
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Copyright (c) 2026 Oscar León Granizo, Valeria Nicol Jaramillo Ramírez, Kevin Javier Lamilla Vera, Cesar Espin Riofrío, Gary Reyes Zambrano (Author)
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