Persona:
Centeno Sánchez, Roberto

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Dirección de correo electrónico
rcenteno@lsi.uned.es
ORCID
0000-0001-9095-4665
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Centeno Sánchez
Nombre de pila
Roberto
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2024 - 20253
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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
    Logic replicant: a new machine learning algorithm for multiclass classification in small datasets
    (IOP Publishing, 2025-04-11) Corral, Pedro; Centeno Sánchez, Roberto; Fresno Fernández, Víctor Diego; Agencia Estatal de Investigación (España); European Commission
    Multiclass classification with small datasets often presents a significant challenge for conventional machine learning (ML) algorithms, predicting with an accuracy affected by this context of data scarcity. To remedy this, this papers presents a novel ML model based on a differentiable deterministic finite-state machine (DFSM) that improves the prediction performance compared with state-of-the-art multiclass classifiers applied in this ambit of small data per class. The proposed model uses a logic-arithmetic function that replicates the inherent classification logic of the problem rather than finding patterns of feature similarity. Our algorithm, called logic replicant, allows to learn problems that other classification models cannot. As the logic replicant is a DFSM it can learn any combinational logic, but it goes beyond this point learning other types of problems such as handwritten-digit recognition, and the detection of mice with Down syndrome based on the presence of 77 proteins. Our ML algorithm is also easy to interpret using quantitative diagrams, in comparison to less interpretable algorithms such as artificial neural networks, random forest, and others. The results obtained with different data sets related to math, physics, biology and image recognition show that our design based on a logic-arithmetic function and being a DFSM improves the generalisation capacity (better prediction accuracy) of the logic replicant compared to other state-of-the-art ML approaches.