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Analysis of institutional authors

Guillen-Pacho, IbaiCorresponding AuthorBadenes-Olmedo, CarlosAuthorCorcho, OscarAuthor

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August 25, 2024
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Dynamic topic modelling for exploring the scientific literature on coronavirus: an unsupervised labelling technique

Publicated to: International Journal of Data Science and Analytics. 20 (3): 2551-2581 - 2025-09-01 20(3), DOI: 10.1007/s41060-024-00610-0

Authors:

Guillén-Pacho, I; Badenes-Olmedo, C; Corcho, O
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Affiliations

Univ Politecn Madrid, Comp Sci Dept, Madrid, Spain - Author
Univ Politecn Madrid, Ontol Engn Grp, Madrid, Spain - Author

Abstract

The work presented in this article focusses on improving the interpretability of probabilistic topic models created from a large collection of scientific documents that evolve over time. Several time-dependent approaches based on topic models were compared to analyse the annual evolution of latent concepts in the CORD-19 corpus: Dynamic Topic Model, Dynamic Embedded Topic Model, and BERTopic. Then COVID-19 period (December 2019-present) has been analysed in greater depth, month by month, to explore the evolution of what is written about the disease. The evaluations suggest that the Dynamic Topic Model is the best choice to analyse the CORD-19 corpus. A novel topic labelling strategy is proposed for dynamic topic models to analyse the evolution of latent concepts. It incorporates content changes in both the annual evolution of the corpus and the monthly evolution of the COVID-19 disease. The generated labels are manually validated using two approaches: through the most relevant documents on the topic and through the documents that share the most semantically similar label topics. The labelling enables the interpretation of topics. The novel method for dynamic topic labelling fits the content of each topic and supports the semantics of the topics.
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Keywords

Cord-1Cord-19CoronavirusCoronavirusesCovid-19Dynamic topic modelDynamic topic modelsInterpretabilityLabeling techniquesLabelingsScientific literatureStem-cell transplantationTimTopic interpretabilityTopic labelingTopic labellingTopic modeling

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal International Journal of Data Science and Analytics due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 128/258, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Computer Science, Information Systems. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Modeling and Simulation.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-26:

  • WoS: 2
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-26:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 24 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/88044/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 135
  • Downloads: 14
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (GUILLÉN PACHO, IBAI) and Last Author (CORCHO GARCIA, OSCAR).

the author responsible for correspondence tasks has been GUILLÉN PACHO, IBAI.

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Awards linked to the item

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work is supported by the DRUGS4COVID++ project, funded by Ayudas Fundacion BBVA a equipos de investigacion cientifica SARS-CoV-2 y COVID-19; and by the Predoctoral Grant (PIPF-2022/COM-25947) of the Consejeria de Educacion, Ciencia y Universidades de la Comunidad de Madrid, Spain.
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