A transformer-based deep learning approach for detecting online hate speech in Spanish

The amount of content published on the Internet has grown exponentially in recent times. Social networks have enabled this content to reach an even wider audience. However, the freedom of communication provided by these networks can consequently facilitate the spread of offensive language and hate speech. Although social media platforms have attempted to implement mechanisms for detecting and addressing such content, it remains an ongoing challenge, particularly for languages other than English, such as Spanish. One promising approach to tackle this problem is the application of Natural Language Processing (NLP) tools, which rely on the use of language models and deep learning for text classification. In this work, an approach for detecting Spanish Hate Speech with ALBETO (SHS-ALBETO) is proposed. Experimentation is conducted with HatEval dataset. The performance of SHS-ALBETO is compared with other competing models, such as BERT, BETO, and DistilBETO, along with other proposals from the state-of-the-art. SHS-ALBETO has improved the existing results in the scientific literature, simultaneously providing reduced computing times. Additionally, analyses of the results have revealed its advantages together with challenging aspects that must be addressed to further improve the performance of this kind of approaches. • The task of detecting online hate speech in Spanish language has been addressed. • A transformer-based deep learning approach (SHS-ALBETO) has been developed. • Experimentation has been carried out with HatEval dataset. • SHS-ALBETO has been compared with competing models and with the state-of-the-art. • The advantages obtained in the performance of SHS-ALBETO have been analyzed.