Dr. Eduardo Silva Alvarado, a researcher at the Universidad Europea del Atlántico (European University of the Atlantic, UNEATLANTICO), is participating in a study that has developed an innovative model for the automatic detection of hate speech on social media. This breakthrough addresses the challenges inherent in identifying offensive content in multilingual and multimodal digital environments.
The rise of social media has facilitated global communication, allowing users to share information in various formats and languages. However, this expansion has also led to the spread of hate speech, which can manifest itself in text, images, and videos, and in multiple languages. Detecting this type of content is essential to maintaining safe and respectful digital environments.
Traditionally, hate speech detection systems have focused on monolingual analysis and a single type of content, mainly text. These approaches have significant limitations when faced with the linguistic diversity and multimodal nature of social media posts. The lack of tools capable of addressing these complexities has made it difficult to effectively identify offensive content in multilingual and multimodal contexts.
The study in question introduces a model that combines advanced natural language processing and image analysis techniques to detect hate speech in multiple languages and content formats. This integrated approach allows for more accurate and efficient identification of offensive content in diverse digital environments.
To develop this model, the team compiled a multilingual and multimodal dataset that includes text, images, and videos with content labeled as hate speech. Deep learning techniques were used to train the model to identify patterns associated with hate speech in different languages and content formats.
Relevant results
The results of the study show that the proposed model outperforms traditional approaches in detecting hate speech in multilingual and multimodal contexts. The model’s ability to simultaneously analyze text and images in multiple languages significantly improves the accuracy and efficiency of identifying offensive content.
This advance has important implications for content moderation on social media platforms, enabling a faster and more effective response to the spread of hate speech. In addition, the model can be adapted to different cultural and linguistic contexts, making it a valuable tool for promoting safer and more inclusive digital environments.
If you would like to learn more about this study, click here.
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