Persona:
Carrillo de Albornoz Cuadrado, Jorge Amando

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Carrillo de Albornoz Cuadrado
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Jorge Amando
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2020 - 20233
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Fecha de finalización: 2023 × Fecha de inicio: 2020 ×

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Mostrando 1 - 3 de 3
  • Miniatura
    Publicación
    Authority and Priority Signals in Automatic Summary Generation for Online Reputation Management
    (Wiley, 2021-05-01) Rodríguez Vidal, Javier; Carrillo de Albornoz Cuadrado, Jorge Amando; Gonzalo Arroyo, Julio Antonio; Plaza Morales, Laura
    Online reputation management (ORM) comprises the collection of techniques that help monitoring and improving the public image of an entity (companies, products, institutions) on the Internet. The ORM experts try to minimize the negative impact of the information about an entity while maximizing the positive material for being more trustworthy to the customers. Due to the huge amount of information that is published on the Internet every day, there is a need to summarize the entire flow of information to obtain only those data that are relevant to the entities. Traditionally the automatic summarization task in the ORM scenario takes some in-domain signals into account such as popularity, polarity for reputation and novelty but exists other feature to be considered, the authority of the people. This authority depends on the ability to convince others and therefore to influence opinions. In this work, we propose the use of authority signals that measures the influence of a user jointly with (a) priority signals related to the ORM domain and (b) information regarding the different topics that influential people is talking about. Our results indicate that the use of authority signals may significantly improve the quality of the summaries that are automatically generated.
  • Miniatura
    Publicación
    Automatic Generation of Entity-Oriented Summaries for Reputation Management
    (Springer, 2020-04-01) Rodríguez Vidal, Javier; Verdejo, Julia; Carrillo de Albornoz Cuadrado, Jorge Amando; Amigo Cabrera, Enrique; Plaza Morales, Laura; Gonzalo Arroyo, Julio Antonio
    Producing online reputation summaries for an entity (company, brand, etc.) is a focused summarization task with a distinctive feature: issues that may affect the reputation of the entity take priority in the summary. In this paper we (i) present a new test collection of manually created (abstractive and extractive) reputation reports which summarize tweet streams for 31 companies in the banking and automobile domains; (ii) propose a novel methodology to evaluate summaries in the context of online reputation monitoring, which profits from an analogy between reputation reports and the problem of diversity in search; and (iii) provide empirical evidence that producing reputation reports is different from a standard summarization problem, and incorporating priority signals is essential to address the task effectively.
  • Miniatura
    Publicación
    A systematic review on media bias detection: What is media bias, how it is expressed, and how to detect it
    (Elsevier, 2023-09-26) Rodrigo Ginés, Francisco Javier; Carrillo de Albornoz Cuadrado, Jorge Amando; Plaza Morales, Laura; https://orcid.org/0000-0001-6235-6860
    Media bias and the intolerance of media outlets and citizens to deal with opposing points of view pose a threat to the proper functioning of democratic processes. In this respect, we present a systematic review of the literature related to media bias detection, in order to characterize and classify the different types of media bias, and to explore the state-of-the-art of automatic media bias detection systems. The main objectives of this paper were twofold. First, we framed information, misinformation and disinformation within a theoretical framework that allows us to differentiate the different existing misinformation problems such as us media bias, fake news, or propaganda. Second, we studied the state of the art of automatic media bias detection systems: analyzing the most recently used techniques and their results, listing the available resources and the most relevant datasets, and establishing a discussion about how to increase the maturity of this area. After doing a comprehensive literature review, we have identified and selected a total of 17 forms of media bias that can be classified depending on the context (e.g., coverage bias, gatekeeping bias, or statement bias), and on the author’s intention (e.g., spin bias, or ideology bias). We also reviewed, following the PRISMA methodology, the main automatic media bias detection systems that have been developed so far, selecting 63 relevant articles, from which we extracted the most used techniques; including non-deep learning methods (e.g., linguistic-based methods, and reported speech-based methods), and deep learning methods (e.g., RNNs-based methods, and transformers-based methods). Additionally, we listed and summarized 18 available datasets for the task of automatic media bias detection. In conclusion, the current methods for automatic media bias detection are still in their infancy and there is still a lot of potential for improvement in terms of accuracy and robustness. We have proposed some future research lines that could potentially contribute to the development of more advanced techniques.