arXiv subjects

Juan Pablo Bascur

Publications and source records attributed to Juan Pablo Bascur.

4 recordsLinked to original sources

arXiv2026

Changing topic bias in biomedical science maps by linking documents through alternative data sources: policy documents, patents, authors, Facebook, and Twitter

Traditional science maps visualize topics by clustering documents within a network, but they are inherently biased toward clustering certain topics over others. If these topics could be chosen, then the science maps could be tailored for different needs. In this paper, we explore the extent to which the topic bias of a science map can be changed by choosing different data sources to build the document network. We analyze this by evaluating the clustering effectiveness of several topic categories over two sources that are traditionally used for the creation of science maps (citations and text similarity) and six non-traditional data sources, which we found favor different kinds of topics: Health issues for Facebook users, biotechnology topics for patent families, government and social issues for policy documents, food topics for Twitter conversations, nursing topics for Twitter users, and geographical entities for document authors (the favoring in this latter source was particularly strong). Our results show that diverse data sources can be used to control topic bias, which opens up the possibility of creating science maps tailored for different needs.

cs.DL↗

arXiv2024

Which topics are best represented by science maps? An analysis of clustering effectiveness for citation and text similarity networks

A science map of topics is a visualization that shows topics identified algorithmically based on the bibliographic metadata of scientific publications. In practice not all topics are well represented in a science map. We analyzed how effectively different topics are represented in science maps created by clustering biomedical publications. To achieve this, we investigated which topic categories, obtained from MeSH terms, are better represented in science maps based on citation or text similarity networks. To evaluate the clustering effectiveness of topics, we determined the extent to which documents belonging to the same topic are grouped together in the same cluster. We found that the best and worst represented topic categories are the same for citation and text similarity networks. The best represented topic categories are diseases, psychology, anatomy, organisms and the techniques and equipment used for diagnostics and therapy, while the worst represented topic categories are natural science fields, geographical entities, information sciences and health care and occupations. Furthermore, for the diseases and organisms topic categories and for science maps with smaller clusters, we found that topics tend to be better represented in citation similarity networks than in text similarity networks.

cs.DL↗

arXiv2023

Academic information retrieval using citation clusters: In-depth evaluation based on systematic reviews

The field of scientometrics has shown the power of citation-based clusters for literature analysis, yet this technique has barely been used for information retrieval tasks. This work evaluates the performance of citation based-clusters for information retrieval tasks. We simulated a search process using these clusters with a tree hierarchy of clusters and a cluster selection algorithm. We evaluated the task of finding the relevant documents for 25 systematic reviews. Our evaluation considered several trade-offs between recall and precision for the cluster selection, and we also replicated the Boolean queries self-reported by the systematic review to serve as a reference. We found that citation-based clusters search performance is highly variable and unpredictable, that it works best for users that prefer recall over precision at a ratio between 2 and 8, and that when used along with query-based search they complement each other, including finding new relevant documents.

cs.DL↗

arXiv2021

Scholars mobility and its impact on the knowledge producers' workforce of European regions

Knowledge production increasingly relies on mobility. However, its role as a mechanism for knowledge recombination and dissemination remains largely unknown. Based on 1,244,080 Web of Science publications from 1,435,729 authors that we used to construct a panel dataset, we study the impact of inter-regional publishing and scientists' mobility in fostering the workforce composition of European countries during 2008-2017. Specifically, we collect information on scientists who have published in one region and then published elsewhere, and explore some determinants of regional and international mobility. Preliminary findings suggest that while talent pools of researchers are increasingly international, their movements seem to be steered by geographical structures. Future research will investigate the impact of mobility on the regional structure of scientific fields by accounting for the appearance and disappearance of research topics over time.

cs.DL↗