Persona:
Rodríguez García, Raquel

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rrodriguez@lsi.uned.es
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Rodríguez García
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Raquel
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2018 - 201912020 - 20252
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Mostrando 1 - 3 de 3
  • Miniatura
    Publicación
    Simulating Misinformation Diffusion on Social Media Through CoNVaI: A Textual- and Agent-Based Diffusion Model
    (International Joint Conferences on Artificial Intelligence, 2025-09) Rodríguez García, Raquel; Centeno Sánchez, Roberto; Rodrigo Yuste, Álvaro; Agencia Estatal de Investigación (España)
    Misinformation has experienced increased online diffusion, leveraging strategies, such as emotional manipulation, to influence users' opinions. Efforts are underway to develop tools to mitigate its effects, such as misinformation propagation models used to simulate the diffusion of information. There are different approaches within these models, although, they show a significant limitation by disregarding the content of the information shared, crucial to the diffusion. We consider it the central aspect of modeling information dissemination. To this end, we focus on Agent-Based Modeling due to its suitability to simulate the complex interactions and heterogeneous behaviors observed on social media. We base our approach on a state-of-the-art Agent-Based Model that we modify and extend to account for the texts of the messages shared, focusing on two aspects that influence agents' decisions: i) the novelty of the content and; ii) its diffusion and behavior over time. To determine whether this content proves informative, we conduct an empirical evaluation using social media data from Twitter. Based on our experimental results, we observe that our textual-based approach reflects information diffusion more realistically than the state of the art, reducing the error regarding real diffusion.
  • Miniatura
    Publicación
    Together we can do it! A roadmap to effectively tackle propaganda-related tasks
    (Emerald, 2024) Rodríguez García, Raquel; Centeno Sánchez, Roberto; Rodrigo Yuste, Álvaro
    Purpose In this paper, we address the need to study automatic propaganda detection to establish a course of action when faced with such a complex task. Although many isolated tasks have been proposed, a roadmap on how to best approach a new task from the perspective of text formality or the leverage of existing resources has not been explored yet. Design/methodology/approach We present a comprehensive study using several datasets on textual propaganda and different techniques to tackle it. We explore diverse collections with varied characteristics and analyze methodologies, from classic machine learning algorithms, to multi-task learning to utilize the available data in such models. Findings Our results show that transformer-based approaches are the best option with high-quality collections, and emotionally enriched inputs improve the results for Twitter content. Additionally, MTL achieves the best results in two of the five scenarios we analyzed. Notably, in one of the scenarios, the model achieves an F1 score of 0.78, significantly surpassing the transformer baseline model’s F1 score of 0.68. Research limitations/implications After finding a positive impact when leveraging propaganda’s emotional content, we propose further research into exploiting other complex dimensions, such as moral issues or logical reasoning. Originality/value Based on our findings, we provide a roadmap for tackling propaganda-related tasks, depending on the types of training data available and the task to solve. This includes the application of MTL, which has yet to be fully exploited in propaganda detection.
  • Miniatura
    Publicación
    MCNP model of the ITER Tokamak Complex
    (Elsevier, 2018-04-11) López Revelles, Antonio Jesús; Catalán Pérez, Juan Pablo; Kolsek, Aljaz; Juárez Mañas, Rafael; Rodríguez García, Raquel; García Camacho, Mauricio; Sanz Gozalo, Javier
    The Tokamak Complex will accommodate the ITER tokamak and some of the plant systems needed for the machine operation. In order to obtain radiation maps in the Tokamak Complex, a new MCNP model was released on September 2016. This model, based on a conservative representation of the latest baseline design, represents a version controlled, computationally stable, user-friendly and easy-to-update and maintain MCNP input of the Tokamak Complex. Every modification of the initial CAD models was reviewed, recorded and version controlled. The MCNP model of the Tokamak Complex uses the most stable MCNP geometry implementations, avoiding the use of universes and macrobodies. The input exhibits a low particle loss rate (<10−9) when running in void with a dispersed isotropic source. It is strongly organized and profusely commented. Information about the level, building, room, system and material is provided in the definition of every cell. The 36862 cells and 57085 surfaces are arranged by levels and by buildings. The cells are also arranged by rooms and by systems, resulting in a room-oriented organization of the model, which allows an easy isolation of every room.