Persona: Plaza Morales, Laura
Cargando...
Dirección de correo electrónico
ORCID
0000-0001-5144-8014
Fecha de nacimiento
Proyectos de investigación
Unidades organizativas
Puesto de trabajo
Apellidos
Plaza Morales
Nombre de pila
Laura
Nombre
9 resultados
Resultados de la búsqueda
Mostrando 1 - 9 de 9
Publicación Detectando Influencers en Medios Sociales utilizando la información de sus seguidores(Sociedad Española para el Procesamiento del Lenguaje Natural, 2020-03) Rodríguez Vidal, Javier; Gonzalo Arroyo, Julio Antonio; Plaza Morales, LauraDada la tarea de encontrar influencers en un dominio dado (i.e. banking) en una red social, en este artículo investigamos (i) la importancia de caracterizar a los seguidores para la detección automática de influencers; (ii) la manera más efectiva de combinar señales obtenidas de los seguidores y de los perfiles principales para la detección automática de influencers. En este trabajo, hemos modelado el discurso usado por los usuarios en dos dominios, automotive y banking, así como el lenguaje utilizado por los influencers en dichos dominios y por sus seguidores, y utilizamos estos Modelos de Lenguaje para estimar la probabilidad de ser un influencer. Nuestro mayor descubrimiento es que los influencers no sólo dependen de su conocimiento sobre el dominio sino del de sus seguidores; por lo tanto, cuanto mayor conocimiento y número de expertos haya entre sus seguidores, mayor será la probabilidad que el perfil sea de un influencer.Publicación Deep-Learning Approach to Educational Text Mining and Application to the Analysis of Topics’ Difficulty(Institute of Electrical and Electronics Engineers, 2020-12-02) Araujo Serna, M. Lourdes; López Ostenero, Fernando; Martínez Romo, Juan; Plaza Morales, LauraLearning analytics has emerged as a promising tool for optimizing the learning experience and results, especially in online educational environments. An important challenge in this area is identifying the most difficult topics for students in a subject, which is of great use to improve the quality of teaching by devoting more effort to those topics of greater difficulty, assigning them more time, resources and materials. We have approached the problem by means of natural language processing techniques. In particular, we propose a solution based on a deep learning model that automatically extracts the main topics that are covered in educational documents. This model is next applied to the problem of identifying the most difficult topics for students in a subject related to the study of algorithms and data structures in a Computer Science degree. Our results show that our topic identification model presents very high accuracy (around 90 percent) and may be efficiently used in learning analytics applications, such as the identification and understanding of what makes the learning of a subject difficult. An exhaustive analysis of the case study has also revealed that there are indeed topics that are consistently more difficult for most students, and also that the perception of difficulty in students and teachers does not always coincide with the actual difficulty indicated by the data, preventing to pay adequate attention to the most challenging topics.Publicación Automatic Recommendation of Forum Threads and Reinforcement Activities in a Data Structure and Programming Course(MDPI, 2023-09-21) Plaza Morales, Laura; Araujo Serna, M. Lourdes; López Ostenero, Fernando; Martínez Romo, JuanOnline learning is quickly becoming a popular choice instead of traditional education. One of its key advantages lies in the flexibility it offers, allowing individuals to tailor their learning experiences to their unique schedules and commitments. Moreover, online learning enhances accessibility to education, breaking down geographical and economical boundaries. In this study, we propose the use of advanced natural language processing techniques to design and implement a recommender that supports e-learning students by tailoring materials and reinforcement activities to students’ needs. When a student posts a query in the course forum, our recommender system provides links to other discussion threads where related questions have been raised and additional activities to reinforce the study of topics that have been challenging. We have developed a content-based recommender that utilizes an algorithm capable of extracting key phrases, terms, and embeddings that describe the concepts in the student query and those present in other conversations and reinforcement activities with high precision. The recommender considers the similarity of the concepts extracted from the query and those covered in the course discussion forum and the exercise database to recommend the most relevant content for the student. Our results indicate that we can recommend both posts and activities with high precision (above 80%) using key phrases to represent the textual content. The primary contributions of this research are three. Firstly, it centers on a remarkably specialized and novel domain; secondly, it introduces an effective recommendation approach exclusively guided by the student’s query. Thirdly, the recommendations not only provide answers to immediate questions, but also encourage further learning through the recommendation of supplementary activities.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, LauraOnline 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.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 AntonioProducing 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.Publicación Deep-Learning Approach to Educational Text Mining and Application to the Analysis of Topics’ Difficulty(Institute of Electrical and Electronics Engineers, 2020-12-02) Araujo Serna, M. Lourdes; López Ostenero, Fernando; Martínez Romo, Juan; Plaza Morales, LauraLearning analytics has emerged as a promising tool for optimizing the learning experience and results, especially in online educational environments. An important challenge in this area is identifying the most difficult topics for students in a subject, which is of great use to improve the quality of teaching by devoting more effort to those topics of greater difficulty, assigning them more time, resources and materials. We have approached the problem by means of natural language processing techniques. In particular, we propose a solution based on a deep learning model that automatically extracts the main topics that are covered in educational documents. This model is next applied to the problem of identifying the most difficult topics for students in a subject related to the study of algorithms and data structures in a Computer Science degree. Our results show that our topic identification model presents very high accuracy (around 90 percent) and may be efficiently used in learning analytics applications, such as the identification and understanding of what makes the learning of a subject difficult. An exhaustive analysis of the case study has also revealed that there are indeed topics that are consistently more difficult for most students, and also that the perception of difficulty in students and teachers does not always coincide with the actual difficulty indicated by the data, preventing to pay adequate attention to the most challenging topics.Publicación Feature engineering for sentiment analysis in e-health forums(Public Library of Science, 2018-11-29) Rodríguez Vidal, Javier; Carrillo de Albornoz Cuadrado, Jorge Amando; Plaza Morales, LauraIntroduction Exploiting information in health-related social media services is of great interest for patients, researchers and medical companies. The challenge is, however, to provide easy, quick and relevant access to the vast amount of information that is available. One step towards facilitating information access to online health data is opinion mining. Even though the classification of patient opinions into positive and negative has been previously tackled, most works make use of machine learning methods and bags of words. Our first contribution is an extensive evaluation of different features, including lexical, syntactic, semantic, network-based, sentiment-based and word embeddings features to represent patient-authored texts for polarity classification. The second contribution of this work is the study of polar facts (i.e. objective information with polar connotations). Traditionally, the presence of polar facts has been neglected and research in polarity classification has been bounded to opinionated texts. We demonstrate the existence and importance of polar facts for the polarity classification of health information. Material and methods We annotate a set of more than 3500 posts to online health forums of breast cancer, crohn and different allergies, respectively. Each sentence in a post is manually labeled as “experience”, “fact” or “opinion”, and as “positive”, “negative” and “neutral”. Using this data, we train different machine learning algorithms and compare traditional bags of words representations with word embeddings in combination with lexical, syntactic, semantic, network-based and emotional properties of texts to automatically classify patient-authored contents into positive, negative and neutral. Beside, we experiment with a combination of textual and semantic representations by generating concept embeddings using the UMLS Metathesaurus. Results We reach two main results: first, we find that it is possible to predict polarity of patient-authored contents with a very high accuracy (≈ 70 percent) using word embeddings, and that this considerably outperforms more traditional representations like bags of words; and second, when dealing with medical information, negative and positive facts (i.e. objective information) are nearly as frequent as negative and positive opinions and experiences (i.e. subjective information), and their importance for polarity classification is crucial.Publicación Automatic Detection of Influencers in Social Networks: Authority versus Domain signals(Wiley, 2019-01-07) Rodríguez Vidal, Javier; Anaya Sánchez, Henry; Gonzalo Arroyo, Julio Antonio; Plaza Morales, LauraGiven the task of finding influencers (opinion makers) for a given domain in a social network, we investigate (a) what is the relative importance of domain and authority signals, (b) what is the most effective way of combining signals (voting, classification, learning to rank, etc.) and how best to model the vocabulary signal, and (c) how large is the gap between supervised and unsupervised methods and what are the practical consequences. Our best results on the RepLab dataset (which improves the state of the art) uses language models to learn the domain-specific vocabulary used by influencers and combines domain and authority models using a Learning to Rank algorithm. Our experiments show that (a) both authority and domain evidence can be trained from the vocabulary of influencers; (b) once the language of influencers is modeled as a likelihood signal, further supervised learning and additional network-based signals only provide marginal improvements; and (c) the availability of training data sets is crucial to obtain competitive results in the task. Our most remarkable finding is that influencers do use a distinctive vocabulary, which is a more reliable signal than nontextual network indicators such as the number of followers, retweets, and so on.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/0000-0001-6235-6860Media 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.