Pub Date : 2025-02-01DOI: 10.1016/j.joi.2024.101635
Chi-shiou Lin , Mu-hsuan Huang , Dar-zen Chen
The phenomenon of multi-affiliation in scientific authorship has increasingly garnered attention in scholarly communication. This study examines the extent and implications of multi-affiliation, distinguishing between intra-institutional and inter-institutional multi-affiliations, including their subtypes based on national and international affiliations. Utilizing data from the Web of Science, the study analyzes scientific papers from well-ranked global universities over a decade (2013–2022). The results indicate a significant prevalence of multi-affiliation, with 22.54 % of authorships and over half of the papers exhibiting at least one instance of multi-affiliation. The study finds notable variations in multi-affiliation trends across countries and subject fields. The findings raise critical questions about the impact of multi-affiliation on research evaluation and university rankings, suggesting a need for refined bibliometric measures and author guidance on affiliations to account for this growing trend.
{"title":"The inter-institutional and intra-institutional multi-affiliation authorships in the scientific papers produced by the well-ranked universities","authors":"Chi-shiou Lin , Mu-hsuan Huang , Dar-zen Chen","doi":"10.1016/j.joi.2024.101635","DOIUrl":"10.1016/j.joi.2024.101635","url":null,"abstract":"<div><div>The phenomenon of multi-affiliation in scientific authorship has increasingly garnered attention in scholarly communication. This study examines the extent and implications of multi-affiliation, distinguishing between intra-institutional and inter-institutional multi-affiliations, including their subtypes based on national and international affiliations. Utilizing data from the Web of Science, the study analyzes scientific papers from well-ranked global universities over a decade (2013–2022). The results indicate a significant prevalence of multi-affiliation, with 22.54 % of authorships and over half of the papers exhibiting at least one instance of multi-affiliation. The study finds notable variations in multi-affiliation trends across countries and subject fields. The findings raise critical questions about the impact of multi-affiliation on research evaluation and university rankings, suggesting a need for refined bibliometric measures and author guidance on affiliations to account for this growing trend.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101635"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.joi.2024.101630
Kai Meng , Zhichao Ba , Chunying Wang , Gang Li
Artificial Intelligence (AI) is experiencing unprecedented innovation and transformation, potentially attributed to intimate interactions between science and technology (S&T) within the field. To identify S&T linkages and detect intrinsic interactions within AI, this paper introduces a network portrait divergence approach, where S&T knowledge networks are prototyped as two-dimensional network portraits based on graph-invariant probability distributions, and comparing them by coupling network portrait divergence with knowledge content. Specifically, S&T knowledge of AI is first extracted and unified through KeyBERT and word-alignment algorithms. Subsequently, temporal S&T knowledge networks are constructed and visualized as two network portraits: node portraits and edge-weight portraits. Network portrait divergence, an information-theoretic, graph-like measure for comparing networks, is applied to calculate varying S&T portrait divergences. Finally, internal knowledge flows within S&T and dynamic interactions between them are unearthed based on multiscale backbone analysis. Empirical experiments on both synthetic networks (random graph ensembles) and real-world AI datasets underscore the feasibility and reliability of the network portrait divergence approach.
{"title":"Unveiling intrinsic interactions of science and technology in artificial intelligence using a network portrait divergence approach","authors":"Kai Meng , Zhichao Ba , Chunying Wang , Gang Li","doi":"10.1016/j.joi.2024.101630","DOIUrl":"10.1016/j.joi.2024.101630","url":null,"abstract":"<div><div>Artificial Intelligence (AI) is experiencing unprecedented innovation and transformation, potentially attributed to intimate interactions between science and technology (S&T) within the field. To identify S&T linkages and detect intrinsic interactions within AI, this paper introduces a network portrait divergence approach, where S&T knowledge networks are prototyped as two-dimensional network portraits based on graph-invariant probability distributions, and comparing them by coupling network portrait divergence with knowledge content. Specifically, S&T knowledge of AI is first extracted and unified through KeyBERT and word-alignment algorithms. Subsequently, temporal S&T knowledge networks are constructed and visualized as two network portraits: node portraits and edge-weight portraits. Network portrait divergence, an information-theoretic, graph-like measure for comparing networks, is applied to calculate varying S&T portrait divergences. Finally, internal knowledge flows within S&T and dynamic interactions between them are unearthed based on multiscale backbone analysis. Empirical experiments on both synthetic networks (random graph ensembles) and real-world AI datasets underscore the feasibility and reliability of the network portrait divergence approach.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101630"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.joi.2024.101632
Guancan Yang , Di Liu , Ling Chen , Kun Lu
Understanding the dynamics of technology convergence is indispensable for both academic and industrial perspectives. Traditional analyses have mainly focused on the link formation process, overlooking the role that persistence process plays in shaping technology networks. This paper endeavors to fill this gap by incorporating the persistence process into the analysis of technology convergence using the Separate Temporal Exponential Random Graph Model (STERGM). Utilizing a decade-long dataset of breast cancer drug patents, we provide a comprehensive view of technology convergence mechanisms and their predictive capabilities. Our findings reveal significant differences in network effects between formation and persistence processes, indicating that focusing on only one may misrepresent the evolution of technology networks. The combined model achieves an F1 score of 69.54% in empirical forecasting, confirming its practical utility. Additionally, we introduce Intensification Networks to examine how existing ties strengthen or weaken over time, uncovering the critical role of intensification in the long-term evolution of technology convergence. By capturing both the formation of new ties and the intensification of existing ones, our model offers a more nuanced and forward-looking understanding of convergence dynamics, particularly in identifying potential areas for future technology convergence.
{"title":"Integrating persistence process into the analysis of technology convergence using STERGM","authors":"Guancan Yang , Di Liu , Ling Chen , Kun Lu","doi":"10.1016/j.joi.2024.101632","DOIUrl":"10.1016/j.joi.2024.101632","url":null,"abstract":"<div><div>Understanding the dynamics of technology convergence is indispensable for both academic and industrial perspectives. Traditional analyses have mainly focused on the link formation process, overlooking the role that persistence process plays in shaping technology networks. This paper endeavors to fill this gap by incorporating the persistence process into the analysis of technology convergence using the <em>Separate Temporal Exponential Random Graph Model</em> (STERGM). Utilizing a decade-long dataset of breast cancer drug patents, we provide a comprehensive view of technology convergence mechanisms and their predictive capabilities. Our findings reveal significant differences in network effects between formation and persistence processes, indicating that focusing on only one may misrepresent the evolution of technology networks. The combined model achieves an F1 score of 69.54% in empirical forecasting, confirming its practical utility. Additionally, we introduce Intensification Networks to examine how existing ties strengthen or weaken over time, uncovering the critical role of intensification in the long-term evolution of technology convergence. By capturing both the formation of new ties and the intensification of existing ones, our model offers a more nuanced and forward-looking understanding of convergence dynamics, particularly in identifying potential areas for future technology convergence.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101632"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.joi.2024.101631
David Melero-Fuentes , Remedios Aguilar-Moya , Juan-Carlos Valderrama-Zurián , Juan Gorraiz
The purpose of the present study is to analyse the presence and evolution in the last 13 years of the document type “Meeting Abstract” in the database where they are best represented, i.e. in the Web of Science Core Collection. We have also studied in which categories and in which type of journals they have a significant presence.
Frequency analyses of meeting abstracts (absolute and ratios) were performed on years, indexes, categories and topics variables, and the Impact Factor was calculated without the citations obtained by the meeting abstracts.
The results obtained show that in disciplines such as Clinical Medicine, Neuroscience & Behavior. and Biology & Biochemistry, they play a very important role due to both their number and the number of attracted citations, and that they are regularly published in top journals, including Q1 according to the Journal of Citation Reports. Our results also corroborate the hypothesis that they inflate the Impact Factor and therefore are one of the reasons for the high absolute values of this indicator in categories like Oncology and Hematology.
This study is of great relevance for researchers and policymakers, because it helps to identify in which disciplines Meeting Abstracts have relevance and they should be considered for the calculation of indicators in bibliometric practices, and opens the door to research into their relationship with other documentary typologies within the social processes of scientific communication in different sciences.
{"title":"Evolution and effect of meeting abstracts in JCR journals","authors":"David Melero-Fuentes , Remedios Aguilar-Moya , Juan-Carlos Valderrama-Zurián , Juan Gorraiz","doi":"10.1016/j.joi.2024.101631","DOIUrl":"10.1016/j.joi.2024.101631","url":null,"abstract":"<div><div>The purpose of the present study is to analyse the presence and evolution in the last 13 years of the document type “Meeting Abstract” in the database where they are best represented, i.e. in the Web of Science Core Collection. We have also studied in which categories and in which type of journals they have a significant presence.</div><div>Frequency analyses of meeting abstracts (absolute and ratios) were performed on years, indexes, categories and topics variables, and the Impact Factor was calculated without the citations obtained by the meeting abstracts.</div><div>The results obtained show that in disciplines such as <em>Clinical Medicine, Neuroscience & Behavior.</em> and <em>Biology & Biochemistry</em>, they play a very important role due to both their number and the number of attracted citations, and that they are regularly published in top journals, including Q1 according to the Journal of Citation Reports. Our results also corroborate the hypothesis that they inflate the Impact Factor and therefore are one of the reasons for the high absolute values of this indicator in categories like <em>Oncology</em> and <em>Hematology.</em></div><div>This study is of great relevance for researchers and policymakers, because it helps to identify in which disciplines Meeting Abstracts have relevance and they should be considered for the calculation of indicators in bibliometric practices, and opens the door to research into their relationship with other documentary typologies within the social processes of scientific communication in different sciences.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101631"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Corrigendum to “A framework armed with node dynamics for predicting technology convergence” [Journal of Informetrics 18 (2024) 101583]","authors":"Guancan Yang , Jiaxin Xing , Shuo Xu , Yuntian Zhao","doi":"10.1016/j.joi.2024.101629","DOIUrl":"10.1016/j.joi.2024.101629","url":null,"abstract":"","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101629"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.joi.2024.101633
Massimo Bilancia , Rade Dačević
Using Bayesian natural language processing (NLP) methods and a scalable variational algorithm tailored for mixtures of discrete positive data, we analyzed a large corpus of 111,411 eprints submitted to the arXiv repository between 1994 and 2022 in the Statistics category (the primary classification for these eprints on arXiv). Our objective is to assess the impact of Machine Learning (ML) on the field of Statistics–specifically, to determine whether the introduction of ML has led to a fundamental paradigm shift, transforming traditional statistical problems or creating entirely new ones, or if this perceived revolution is primarily occurring outside the field of Statistics. Our findings suggest that the only significant paradigm shift for Statistics as a scientific discipline remains the Bayesian revolution that began in the early 1990s.
{"title":"A Dirichlet-Multinomial mixture model of Statistical Science: Mapping the shift of a paradigm","authors":"Massimo Bilancia , Rade Dačević","doi":"10.1016/j.joi.2024.101633","DOIUrl":"10.1016/j.joi.2024.101633","url":null,"abstract":"<div><div>Using Bayesian natural language processing (NLP) methods and a scalable variational algorithm tailored for mixtures of discrete positive data, we analyzed a large corpus of 111,411 eprints submitted to the arXiv repository between 1994 and 2022 in the Statistics category (the primary classification for these eprints on arXiv). Our objective is to assess the impact of Machine Learning (ML) on the field of Statistics–specifically, to determine whether the introduction of ML has led to a fundamental paradigm shift, transforming traditional statistical problems or creating entirely new ones, or if this perceived revolution is primarily occurring outside the field of Statistics. Our findings suggest that the only significant paradigm shift for Statistics as a scientific discipline remains the Bayesian revolution that began in the early 1990s.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101633"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-26DOI: 10.1016/j.joi.2025.101638
Wolfgang G. Stock , Gerhard Reichmann , Christian Schlögl
Describing, analyzing, and evaluating research institutions are among the main tasks of scientometrics and research evaluation. But how can we optimally search for an institution's research output? Possible search arguments include institution names, affiliations, addresses, and affiliated authors’ names. Prerequisites of these search tasks are complete lists (or at least good approximations) of the institutions’ publications, and—in later steps—their citations, and topics. When searching for the publications of research institutions in an information service, there are two options, namely (1) searching directly for the name of the institution and (2) searching for all authors affiliated with the institution in a defined time interval. Which strategy is more effective? More specifically, do informetric indicators such as recall and precision, search recall and search precision, and relative visibility change depending on the search strategy? What are the reasons for differences? To illustrate our approach, we conducted an illustrative study on two information science institutions and identified all staff members. The search was performed using the Web of Science Core Collection (WoS CC). As a performance indicator, applying fractional counting and considering co-affiliations of authors, we used the institution's relative visibility in an information service. We also calculated two variants of recall and precision at the institution level, namely search recall and search precision as informetric measures of performance differences between different search strategies (here: author search versus institution search) on the same information service (here: WoS CC) and recall and precision in relation to the complete set of an institution's publications. For all our calculations, there is a clear result: Searches for affiliated authors outperform searches for institutions in WoS. However, especially for large institutions it is difficult to determine all the staff members in the time interval of research. Additionally, information services (including WoS) are incomplete and there are variants for the names of institutions in the services. Therefore, searching for institutions and the publication-based quantitative evaluation of institutions are very critical issues.
{"title":"Investigating the research output of institutions","authors":"Wolfgang G. Stock , Gerhard Reichmann , Christian Schlögl","doi":"10.1016/j.joi.2025.101638","DOIUrl":"10.1016/j.joi.2025.101638","url":null,"abstract":"<div><div>Describing, analyzing, and evaluating research institutions are among the main tasks of scientometrics and research evaluation. But how can we optimally search for an institution's research output? Possible search arguments include institution names, affiliations, addresses, and affiliated authors’ names. Prerequisites of these search tasks are complete lists (or at least good approximations) of the institutions’ publications, and—in later steps—their citations, and topics. When searching for the publications of research institutions in an information service, there are two options, namely (1) searching directly for the name of the institution and (2) searching for all authors affiliated with the institution in a defined time interval. Which strategy is more effective? More specifically, do informetric indicators such as recall and precision, search recall and search precision, and relative visibility change depending on the search strategy? What are the reasons for differences? To illustrate our approach, we conducted an illustrative study on two information science institutions and identified all staff members. The search was performed using the Web of Science Core Collection (WoS CC). As a performance indicator, applying fractional counting and considering co-affiliations of authors, we used the institution's relative visibility in an information service. We also calculated two variants of recall and precision at the institution level, namely search recall and search precision as informetric measures of performance differences between different search strategies (here: author search versus institution search) on the same information service (here: WoS CC) and recall and precision in relation to the complete set of an institution's publications. For all our calculations, there is a clear result: Searches for affiliated authors outperform searches for institutions in WoS. However, especially for large institutions it is difficult to determine all the staff members in the time interval of research. Additionally, information services (including WoS) are incomplete and there are variants for the names of institutions in the services. Therefore, searching for institutions and the publication-based quantitative evaluation of institutions are very critical issues.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101638"},"PeriodicalIF":3.4,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-23DOI: 10.1016/j.joi.2025.101640
Dimity Stephen
This study investigates the viability of distinguishing articles in questionable journals (QJs) from those in non-QJs on the basis of quantitative indicators typically associated with quality. Subsequently, I examine what can be deduced about the quality of articles in QJs based on the differences observed. The samples comprise 1,714 articles from 31 QJs, 1,691 articles from 16 journals indexed in Web of Science (WoS), and 1,900 articles from 45 mid-tier journals, all in the field of psychology. I contrast between samples the length of abstracts and full-texts, prevalence of spelling errors, text readability, number of references and citations, the size and internationality of the author team, the documentation of ethics and informed consent statements, and the presence of statistical errors. The results suggest that QJ articles do diverge from the disciplinary standards set by peer-reviewed journals in psychology on quantitative indicators of quality that tend to reflect the effect of peer review and editorial processes. However, mid-tier and WoS journals are also affected by potential quality concerns, such as under-reporting of ethics and informed consent processes and the presence of errors in interpreting statistics. Further research is required to develop a comprehensive understanding of the quality of articles in QJs.
{"title":"Distinguishing articles in questionable and non-questionable psychology journals using quantitative indicators associated with quality","authors":"Dimity Stephen","doi":"10.1016/j.joi.2025.101640","DOIUrl":"10.1016/j.joi.2025.101640","url":null,"abstract":"<div><div>This study investigates the viability of distinguishing articles in questionable journals (QJs) from those in non-QJs on the basis of quantitative indicators typically associated with quality. Subsequently, I examine what can be deduced about the quality of articles in QJs based on the differences observed. The samples comprise 1,714 articles from 31 QJs, 1,691 articles from 16 journals indexed in Web of Science (WoS), and 1,900 articles from 45 mid-tier journals, all in the field of psychology. I contrast between samples the length of abstracts and full-texts, prevalence of spelling errors, text readability, number of references and citations, the size and internationality of the author team, the documentation of ethics and informed consent statements, and the presence of statistical errors. The results suggest that QJ articles do diverge from the disciplinary standards set by peer-reviewed journals in psychology on quantitative indicators of quality that tend to reflect the effect of peer review and editorial processes. However, mid-tier and WoS journals are also affected by potential quality concerns, such as under-reporting of ethics and informed consent processes and the presence of errors in interpreting statistics. Further research is required to develop a comprehensive understanding of the quality of articles in QJs.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101640"},"PeriodicalIF":3.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-23DOI: 10.1016/j.joi.2025.101643
Lili Qiao , Star X. Zhao , Yutong Ji , Wu Li
Based on the underlying usage data given by the Web of Science, we establish a novel metric, termed Uh-index for multi-dimensional assessment of academic journals. Our research objectively examines the empirical and theoretical dimensions of the Uh-index, assessing its validity and potential use in scientific evaluation. For this study, we conducted a quantitative analysis of the Uh-index for 1,603 journals across the fields of physics, chemistry, economics, and management, and explored potential theory models. It reveals that the Uh-index, as a literature metric based on usage data, is more sensitive and discriminatory compared to the h-index, which relies solely on citation data. Additionally, the Uh-index and paper usage data were consistent with both the Glänzel–Schubert and the power-law model. It indicates that the Uh index, as an impact observatory index, aligns with the fundamental principles of scientific knowledge dissemination, thereby holding significant scientific value. It facilitates the quantification of dissemination characteristics of core articles in journals, laying the foundation for a novel approach to categorizing and evaluating journals based on both theoretical orientation and practical application. Finally, from a multidimensional research evaluation perspective, the Uh index offers a transitional dimension for observation, bridging the gap between academic citations and the broader dissemination of research through on social media.
{"title":"A measure and the related models for characterizing the usage of academic journal","authors":"Lili Qiao , Star X. Zhao , Yutong Ji , Wu Li","doi":"10.1016/j.joi.2025.101643","DOIUrl":"10.1016/j.joi.2025.101643","url":null,"abstract":"<div><div>Based on the underlying usage data given by the <em>Web of Science</em>, we establish a novel metric, termed U<sub>h</sub>-index for multi-dimensional assessment of academic journals. Our research objectively examines the empirical and theoretical dimensions of the U<sub>h</sub>-index, assessing its validity and potential use in scientific evaluation. For this study, we conducted a quantitative analysis of the U<sub>h</sub>-index for 1,603 journals across the fields of physics, chemistry, economics, and management, and explored potential theory models. It reveals that the U<sub>h</sub>-index, as a literature metric based on usage data, is more sensitive and discriminatory compared to the h-index, which relies solely on citation data. Additionally, the U<sub>h</sub>-index and paper usage data were consistent with both the Glänzel–Schubert and the power-law model. It indicates that the U<sub>h</sub> index, as an impact observatory index, aligns with the fundamental principles of scientific knowledge dissemination, thereby holding significant scientific value. It facilitates the quantification of dissemination characteristics of core articles in journals, laying the foundation for a novel approach to categorizing and evaluating journals based on both theoretical orientation and practical application. Finally, from a multidimensional research evaluation perspective, the U<sub>h</sub> index offers a transitional dimension for observation, bridging the gap between academic citations and the broader dissemination of research through on social media.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101643"},"PeriodicalIF":3.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-22DOI: 10.1016/j.joi.2025.101639
Yejin Park , Seonkyu Lim , Changdai Gu , Arida Ferti Syafiandini , Min Song
Topic trends in rapidly evolving domains like blockchain are dynamic and pose prediction challenges. To address this, we propose a novel framework that integrates topic modeling, clustering, and time-series deep learning models. These models include both non-graph-based and graph-based approaches. Blockchain-related documents of three types—academic papers, patents, and news articles—are collected and preprocessed. Random and topic subgraphs are constructed as inputs for model training and forecasting across various time epochs. The four models (LSTM, GRU, AGCRN, and A3T-GCN) are trained on random subgraphs, and the trained models forecast topic trends using topic subgraphs. We also analyze the distinctive characteristics of each document type and investigate the causal relationships between them. The results indicate that non-graph-based models, such as LSTM, perform better on periodic data like academic papers, whereas graph-based models, such as AGCRN and A3T-GCN, excel at capturing non-periodic patterns in patents and news articles. Our framework demonstrates robust performance, offering a versatile tool for blockchain-related trend analysis and forecasting. The code and environments are available at https://github.com/textmining-org/topic-forecasting.
{"title":"Forecasting topic trends of blockchain utilizing topic modeling and deep learning-based time-series prediction on different document types","authors":"Yejin Park , Seonkyu Lim , Changdai Gu , Arida Ferti Syafiandini , Min Song","doi":"10.1016/j.joi.2025.101639","DOIUrl":"10.1016/j.joi.2025.101639","url":null,"abstract":"<div><div>Topic trends in rapidly evolving domains like blockchain are dynamic and pose prediction challenges. To address this, we propose a novel framework that integrates topic modeling, clustering, and time-series deep learning models. These models include both non-graph-based and graph-based approaches. Blockchain-related documents of three types—academic papers, patents, and news articles—are collected and preprocessed. Random and topic subgraphs are constructed as inputs for model training and forecasting across various time epochs. The four models (LSTM, GRU, AGCRN, and A3T-GCN) are trained on random subgraphs, and the trained models forecast topic trends using topic subgraphs. We also analyze the distinctive characteristics of each document type and investigate the causal relationships between them. The results indicate that non-graph-based models, such as LSTM, perform better on periodic data like academic papers, whereas graph-based models, such as AGCRN and A3T-GCN, excel at capturing non-periodic patterns in patents and news articles. Our framework demonstrates robust performance, offering a versatile tool for blockchain-related trend analysis and forecasting. The code and environments are available at <span><span>https://github.com/textmining-org/topic-forecasting</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101639"},"PeriodicalIF":3.4,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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