arXiv subjects

Victor Herrero-Solana

Publications and source records attributed to Victor Herrero-Solana.

4 recordsLinked to original sources

arXiv2026

Artificial Intelligence Specialization in the European Union: Underexplored Role of the Periphery at NUTS-3 Level

This study examines the distribution of Artificial Intelligence (AI) research across European NUTS-3 regions during the period 2015-2024. Using bibliometric data from Clarivate InCites and the Citation Topics classification system, we analyse two hierarchical thematic levels: Electrical Engineering, Electronics & Computer Science (Macro Citation Topic 4) and Artificial Intelligence & Machine Learning (Meso Citation Topic 4.61). Relative Specialization Index (RSI) and Relative Citation Impact (RCI) indicators are calculated for 781 European NUTS-3 regions. While major metropolitan hubs such as Paris, Warszawa, and Madrid dominate in absolute publication volume, the results reveal that the highest levels of relative AI specialization are concentrated in peripheral regions, particularly in Eastern Europe and Spain. Granada and Vilniaus apskritis stand out as regions combining high specialization with strong citation visibility. The analysis further suggests a weak relationship between regional specialization and citation impact, revealing multiple regional profiles, including highly specialized regions with limited citation visibility, highly visible regions with comparatively low specialization, and diversified scientific systems combining moderate specialization with strong citation impact. Fyn emerges as an extreme case of very high citation impact despite relatively low specialization.

cs.DL↗

arXiv2026

News Harvesting from Google News combining Web Scraping, LLM Metadata Extraction and SCImago Media Rankings enrichment: a case study of IFMIF-DONES

This study develops and evaluates a systematic methodology for constructing news datasets from Google News, combining automated web scraping, large language model (LLM)-based metadata extraction, and SCImago Media Rankings enrichment. Using the IFMIF-DONES fusion energy project as a case study, we implemented a five-stage data collection pipeline across 81 region-language combinations, yielding 1,482 validated records after a 56% noise reduction. Results are compared against two licensed press databases: MyNews (2,280 records) and ProQuest Newsstream Collection (148 records). Overlap analysis reveals high complementarity, with 76% of Google News records exclusive to this platform. The dataset captures content types absent from proprietary databases, including specialized outlets, institutional communications, and social media posts. However, significant methodological challenges emerge: temporal instability requiring synchronic collection, a 100-result cap per query demanding multi-stage strategies, and unexpected noise including academic PDFs, false positives, and pornographic content infiltrating results through black hat SEO techniques. LLM-assisted extraction proved effective for structured articles but exhibited systematic hallucination patterns requiring validation protocols. We conclude that Google News offers valuable complementary coverage for communication research but demands substantial methodological investment, multi-source triangulation, and robust filtering mechanisms to ensure dataset integrity.

cs.DL↗

arXiv2024

Improving Tag-Clouds as Visual Information Retrieval Interfaces

Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to refinding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud's tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.

cs.IR↗

arXiv2010

Worldwide topology of the scientific subject profile: a macro approach on the country level

Models for the production of knowledge and systems of innovation and science are key elements for characterizing a country in view of its scientific thematic profile. With regard to scientific output and publication in journals of international visibility, the countries of the world may be classified into three main groups according to their thematic bias. This paper aims to classify the countries of the world in several broad groups, described in terms of behavioural models that attempt to sum up the characteristics of their systems of knowledge and innovation. We perceive three clusters in our analysis: 1) the biomedical cluster, 2) the basic science & engineering cluster, and 3) the agricultural cluster. The countries are conceptually associated with the clusters via Principal Component Analysis (PCA), and a Multidimensional Scaling (MDS) map with all the countries is presented.

cs.DL↗