Persona:
Plaza Morales, Laura

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ORCID
0000-0001-5144-8014
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Plaza Morales
Nombre de pila
Laura
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2023 - 20242
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Autor: Carrillo de Albornoz Cuadrado, Jorge Amando × Fecha de inicio: 2023 ×

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Mostrando 1 - 2 de 2
  • 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.
  • Miniatura
    Publicación
    Leveraging Unsupervised Task Adaptation and Semi-Supervised Learning With Semantic-Enriched Representations for Online Sexism Detection
    (Wiley, 2024-10-25) Rodríguez-Sánchez, Francisco; Carrillo de Albornoz Cuadrado, Jorge Amando; Plaza Morales, Laura; https://orcid.org/0000-0002-4669-5261
    Over the past decade, the proliferation of hateful and sexist content targeting women on social media has become a concerning issue, adversely affecting women's lives and freedom of expression. Previous efforts to detect online sexism have utilized monolingual ensemble transformers combined with data augmentation techniques that incorporate related-domain data, such as hate speech. However, these approaches often struggle to capture the full diversity and complexity of sexism due to limitations in the size and quality of training data. In this study, we introduce a novel sexism detection system that employs in-domain unlabeled data through unsupervised task-adaptation techniques and semi-supervised learning, using an efficient single multilingual transformer model. Additionally, we incorporate a Sentence-BERT layer to enhance our system with semantically meaningful sentence embeddings. Our proposed system outperforms existing state-of-the-art methods across all tasks and datasets, demonstrating its effectiveness in detecting and addressing sexism in social media text. These results underscore the potential of our approach, providing a foundation for further research and practical applications.