The article Contextualization of a Radical Language Detection System Through Moral Values and Emotions has been recently published in the IEEE Access journal (JCR Q2 2022, 3.9 IF). The publicacion is authored by Patricia Alonso and Oscar Araque. This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 962547.

The full paper can be found here.



The popularity of current communication technologies has boosted the spread of polarization and radical ideologies, which can be exploited by terrorist organizations. Building upon previous research, this work focuses on the task of automatic radicalization detection in texts using natural language processing and machine learning techniques. In this way, we investigate the effectiveness of integrating moral values through the Moral Foundations Theory (MFT). Moral values play a crucial role in identifying ideological inclinations and can have a significant impact on the radicalization detection task. Our approach distinguishes itself in the feature extraction stage, leveraging moral values, emotions, and similarity-based features that utilize word embeddings. Additionally, we thoroughly evaluate the proposed representations with three distinct datasets that model radicalization and use the SHAP method to gain relevant insight into the models’ reasoning.

GSI is participating in the final conference of the project PARTICIPATION in Rome. The conference showcases the innovative and participatory methods and tools that the project has developed and tested for analysing and preventing radicalization and violent extremism in various contexts and countries over the past three years. The conference has two main themes: conspiracy theories and radicalization trends, and the role of civil society and institutions in preventing violent extremism. 

The article "Detection of the Severity Level of Depression Signs in Text Combining a Feature-Based Framework with Distributional Representations ", by Sergio Muñoz and Carlos A. Iglesias has been published in the Applied Sciences journal (2.7 impact factor, JCR Q2 2022). This work is a product of the MIRATAR and AMOR projects.

The full paper can be found at this URL.



Depression is a common and debilitating mental illness affecting millions of individuals, diminishing their quality of life and overall well-being. The increasing prevalence of mental health disorders has underscored the need for innovative approaches to detect and address depression. In this context, text analysis has emerged as a promising avenue. Novel solutions for text-based depression detection commonly rely on deep neural networks or transformer-based models. Although these approaches have yielded impressive results, they often come with inherent limitations, such as substantial computational requirements or a lack of interpretability. This work aims to bridge the gap between substantial performance and practicality in the detection of depression signs within digital content. To this end, we introduce a comprehensive feature framework that integrates linguistic signals, emotional expressions, and cognitive patterns. The combination of this framework with distributional representations contributes to fostering the understanding of language patterns indicative of depression and provides a deeper grasp of contextual nuances. We exploit this combination using traditional machine learning methods in an effort to yield substantial performance without compromising interpretability and computational efficiency. The performance and generalizability of our approach have been assessed through experimentation using multiple publicly available English datasets. The results demonstrate that our method yields throughput on par with more complex and resource-intensive solutions, achieving F1-scores above 70%. This accomplishment is notable, as the proposed method simultaneously preserves the virtues of simplicity, interpretability, and reduced computational overhead. In summary, the findings of this research contribute to the field by offering an accessible and scalable solution for the detection of depression in real-world scenarios.



Es un placer felicitar a nuestro compañero Juan Ramón Velasco por el merecido Premio de Investigación e Innovación de CLM 2022. ¡Felicidades!


Juan Ramón Velasco, catedrático de la UAH y guadalajareño de adopción, recogerá el Premio Investigación e Innovación de CLM 2022

Juan Ramón Velasco es ingeniero de Telecomunicaciones y catedrático de la UAH. Imagen: Universidad de Alcalá

El profesor de la UAH recibirá el galardón en la categoría de Ingeniería y Arquitectura el próximo lunes, 6 de marzo


El Gobierno regional entregará los Premios de Investigación e Innovación de Castilla-La Mancha 2022 el 6 de marzo, lunes, en un acto que tendrá lugar en la Facultad de Farmacia de Albacete. Unos galardones con los que se reconoce y premia el esfuerzo, la calidad y la excelencia en el ámbito de la investigación y la actividad científica.


De ello ha informado la consejera de Igualdad y portavoz, Blanca Fernández, que ha detallado el resultado de la deliberación del jurado de estos premios que se traduce en la entrega de 16 galardones, repartidos en siete categorías, “a personas con trayectorias muy importantes, con mucho potencial investigador y reconocidas a nivel nacional e internacional”, ha dicho la consejera, que ha mostrado su orgullo por el “mucho talento que tenemos en Castilla-La Mancha”.


Comienza el proyecto MIRATAR cuyo objetivo es mejorar la calidad de vida de las personas ancianas mediante la detección temprana de la fragilidad. GSI participa con el desarrollo de modelos de inteligencia artificial para detectar factores de fragilidad y realizar recomendaciones para retrasar su aparición.