Journal of Informetrics最新文献

{"title":"SMIAltmetric: A comprehensive metric for evaluating social media impact of scientific papers on Twitter (X)","authors":"Zuzheng Wang ,&nbsp;Yongxu Lu ,&nbsp;Yuanyuan Zhou ,&nbsp;Jiaojiao Ji","doi":"10.1016/j.joi.2024.101562","DOIUrl":"10.1016/j.joi.2024.101562","url":null,"abstract":"<div><p>The rise of social media has significantly influenced scholarly communication, knowledge dissemination, and research evaluation, leading to the enrichment of alternative metrics (altmetrics) for evaluating academic papers’ social impact, which assesses the social impact of academic papers through online activities, including reading, bookmarking, downloading, and commenting. However, these altmetrics often focus on the number of mentions on social media rather than thoroughly evaluating the source, content, and dissemination of these mentions. To address this gap, this study introduces the social media impact altmetric (SMIAltmetric), which is based on 44,087 publications and 860,680 tweets (now “posts”), a comprehensive scoring system for evaluating scientific papers on Twitter (now “X”), using diverse features, including literature-related, social media engagement-related, user-related, and content-related features. Employing Altmetric Attention Acores (AAS) as labels, we tested eight machine learning algorithms, with XGBoost demonstrating the highest accuracy at 0.8672. Crucial factors influencing SMIAltmetric, as identified by the SHAP value, were followers, retweets, mentions, and citation. Furthermore, consistency analysis and convergent validation between the proposed SMIAltmetric and AAS confirm the reliability and finer differentiation of SMIAltmetric. The proposed SMIAltmetric provides a more comprehensive understanding of a paper’s social media impact, enhancing the evaluation of scientific discourse and its engagement with society.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101562"},"PeriodicalIF":3.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949699","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":"Integrating prior field knowledge as key documents with main path analysis utilizing key-node path search","authors":"Chung-Huei Kuan","doi":"10.1016/j.joi.2024.101569","DOIUrl":"10.1016/j.joi.2024.101569","url":null,"abstract":"<div><p>The integration of prior field knowledge in analytical or modeling processes is generally considered favorable across various disciplines, yet its utilization in Main Path Analysis (MPA) has been limited to gathering documents or validating the obtained main paths (MPs). This study envisions that prior knowledge about a field can be embodied in certain key documents that are considered seminal or crucial to the field's development. A so-called key-node path search is then employed to produce MPs that capture a distinct knowledge flow centering around these key documents. This study further proposes a unified approach that automatically and simultaneously produces the key-document MPs alongside the traditional MPs. Through this unified approach, the focused knowledge flow through the key documents and the field's overall knowledge flow, as revealed by the traditional MPs, can be concurrently observed to see how they interact, thereby providing additional insights into the field's development. Not only may the key-document MPs capture a meaningful development trajectory, but their complement to the traditional MPs can also hint at their respective representativeness. To establish this unified approach, this study formally demonstrates how the traditional MPs can be produced with key-node path searches, enabling their simultaneous creation alongside the key-document MPs. A case study is conducted based on patents in the field of Evolutionary Computation from an official artificial intelligence patent dataset to demonstrate the application of this unified approach.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101569"},"PeriodicalIF":3.4,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141954428","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":"Tree knowledge structure for better insight: Capturing biomedical science-technology knowledge linkage with MeSH","authors":"Zhejun Zheng ,&nbsp;Yaxue Ma ,&nbsp;Zhichao Ba ,&nbsp;Lei Pei","doi":"10.1016/j.joi.2024.101568","DOIUrl":"10.1016/j.joi.2024.101568","url":null,"abstract":"<div><p>Measuring the knowledge linkage between science and technology (S&amp;T) is crucial for understanding the interactions between S&amp;T and assisting decision-makers in strategizing research and development investments. Conventional analyses of S&amp;T knowledge linkage have frequently overlooked the semantic structure of knowledge elements thereby introducing biases in the measurements. To address this issue, this study introduces a novel method predicated on the tree semantic structure, which quantifies the S&amp;T linkage by considering the hierarchy and category of knowledge elements within an ontological framework. In this method, knowledge trees are constructed to represent the core knowledge of S&amp;T literature, incorporating hierarchically organized MeSH descriptors. These knowledge trees are subsequently utilized to measure the knowledge linkage between S&amp;T by integrating intra-branch knowledge similarity and inter-branch knowledge distribution. An empirical analysis was conducted on a substantial corpus of scientific publications and patents within the biomedicine sector. The findings predominantly revealed a stronger knowledge linkage between S&amp;T in recent years, relative to the early 2000 s. It was also observed that patents are more inclined to include broader concepts in their titles and abstracts, in contract to the more specific concepts found in scientific publications. S&amp;T literatures have increasingly focused on knowledge related to diseases, equipment, and health care. To verify the reliability of the proposed method, validation was performed with alternative measurements of knowledge linkage. In comparison to single-feature-based linkage measurements and network-based approaches, our proposed method demonstrates superior adaptability in capturing S&amp;T linkage, especially when there is a marked disparity in the sample sizes of S&amp;T literature. This study not only enriches the measurements of S&amp;T knowledge linkage, but also furnishes empirical insights into the evolving patterns of S&amp;T linkage within the biomedical domain.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101568"},"PeriodicalIF":3.4,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141947143","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":"Equivalence of inequality indices in the three-dimensional model of informetric impact","authors":"Lucio Bertoli-Barsotti ,&nbsp;Marek Gagolewski ,&nbsp;Grzegorz Siudem ,&nbsp;Barbara Żogała-Siudem","doi":"10.1016/j.joi.2024.101566","DOIUrl":"10.1016/j.joi.2024.101566","url":null,"abstract":"<div><p>Inequality is an inherent part of our lives: we see it in the distribution of incomes, talents, citations, to name a few. However, its intensity varies across environments: there are systems where the available resources are relatively evenly distributed but also where a small group of items or agents controls the majority of assets. Numerous indices for quantifying the degree of inequality have been proposed but in general, they work quite differently.</p><p>We recently observed (<span><span>Siudem et al., 2020</span></span>) that many rank-size distributions might be approximated by a time-dependent agent-based model involving a mixture of preferential (rich-get-richer) and accidental (sheer chance) attachment. In this paper, we point out its relationship to an iterative process that generates rank distributions of any length and a predefined level of inequality, as measured by the Gini index.</p><p>We prove that, under our model, the Gini, Bonferroni, De Vergottini, and Hoover indices are equivalent for samples of similar sizes. Given one of them, we can recreate the value of another measure. Thanks to the obtained formulae, we can also understand how they depend on the sample size. An empirical analysis of a large database of citation records in economics (RePEc) yields a good match with our theoretical derivations.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101566"},"PeriodicalIF":3.4,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953344","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":"International collaboration leading to high citations: Global impact or home country effect?","authors":"Jue Wang ,&nbsp;Rainer Frietsch ,&nbsp;Peter Neuhäusler ,&nbsp;Rosalie Hooi","doi":"10.1016/j.joi.2024.101565","DOIUrl":"10.1016/j.joi.2024.101565","url":null,"abstract":"<div><p>Scientific research has become more collaborative, which brings a number of advantages including higher citation rates. This study examines the factors contributing to higher citations by distinguishing between the quality of work and the home country effect. Using international co-authorship as a key variable, we analyze citation patterns across a diverse range of fields over a 10-year period, and differentiate between citations accrued in the authors’ countries and citations received in other countries. The results demonstrate the presence of both global impact and a home country effect. Specifically, publications with international co-authorship receive significantly more citations from abroad, which strongly implies that international collaboration fosters high quality research and positively impacts citation rates, especially when considering the relatively smaller foreign community size once the authors’ home countries are excluded. On the other hand, it is also observed that domestic citations from authors’ countries increase faster than foreign citations and the effect is more pronounced over a longer period of time, which suggests that home country effect plays an important role in accumulating citations through the increased visibility in the domestic research community. The study confirms the pivotal role of international collaboration in research impact and highlights the significance of network building.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101565"},"PeriodicalIF":3.4,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141960548","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":"The Prize Winner Index (PWI): A proposal for an indicator based on scientific prizes","authors":"Lutz Bornmann ,&nbsp;Robin Haunschild","doi":"10.1016/j.joi.2024.101560","DOIUrl":"10.1016/j.joi.2024.101560","url":null,"abstract":"<div><p>In this study, we propose a new index for measuring performance in science which is based on collaborations (co-authorship distances) in science: the Prize Winner Index (PWI). The PWI is based on the Erdős number – a number that was introduced several years ago. We propose to focus with the new index on laureates of prestigious prizes in a certain field and to measure co-authorship distances between the laureates and other scientists. To exemplify and explain our proposal, we computed the proposed index in the field of quantitative science studies (PWI_PM). The Derek de Solla Price Memorial Award (Price Medal, PM) is awarded to outstanding scientists in the field. We tested the convergent validity of the PWI_PM. We were interested whether the indicator is related to two established bibliometric indicators: (1) citation impact (number of papers belonging to the 10 % most frequently cited), and (2) journal prestige (number of papers which have appeared in top quartile journals). The results show that the coefficients for the correlation between PWI_PM and both indicators are high in cases when a sufficient number of papers have been considered for a reliable assessment of performance. Therefore, measured by established indicators for research performance, the new PWI indicator seems to be convergently valid and, therefore, might be a possible alternative for established (bibliometric) indicators – with a focus on prizes.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101560"},"PeriodicalIF":3.4,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1751157724000737/pdfft?md5=a78f0a37ede71b9200e3410c447d7ef0&pid=1-s2.0-S1751157724000737-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953343","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}

{"title":"The determinants and impact of research grants: The case of Brazilian productivity scholarships","authors":"Marcelo Perlin,&nbsp;Denis Borenstein,&nbsp;Takeyoshi Imasato,&nbsp;Marcos Reichert","doi":"10.1016/j.joi.2024.101563","DOIUrl":"10.1016/j.joi.2024.101563","url":null,"abstract":"<div><p>Research Productivity Grant (PQ) is a governmental research award maintained by CPNq, the Brazilian Council of Research, and designed as a funding program to support scientific studies in all fields of science. Using a compilation of data from the Lattes platform, we study the individual CVs of more than 133000 researchers between 2005 and 2022 to examine PQ's selection criteria and impact on research productivity over time. First, a machine learning model can accurately predict who receives the financial support. This suggests that some parts of the evaluation process can be automated based on Lattes. Moreover, the main factors that impact the likelihood of a researcher receiving an entry-level PQ are the number of supervisions and papers published. These factors are consistent across different fields of science. Additionally, we found a significant and positive impact from receiving the award in key academic research output. After receiving a CNPq productivity award, researchers tend to increase the number of citations of papers and publications.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101563"},"PeriodicalIF":3.4,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141951290","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":"Comparing patent in-text and front-page references to science","authors":"Jian Wang ,&nbsp;Suzan Verberne","doi":"10.1016/j.joi.2024.101564","DOIUrl":"10.1016/j.joi.2024.101564","url":null,"abstract":"<div><p>Patent references to science provide a paper trail of knowledge flow from science to innovation, and have attracted a lot of attention in recent years. However, we understand little about the differences between two types of patents references: front-page vs. in-text. While both types of references are becoming more accessible, we still lack a systematic understanding on how results are sensitive to which type of references are being analyzed in science and innovation studies. Using a dataset of 33,337 USPTO biotech utility patents, their 860,879 in-text and 637,570 front-page references to Web of Science journal articles, we found a remarkable low overlap between these two types of references. We also found that in-text references are more basic and have more scientific citations than front-page references. The difference in interdisciplinarity and novelty is small when comparing at the reference level and insignificant when comparing at the patent level. We analyze the association between patent value (as measured by patent citations and market value) and characteristics of referenced sciences. Results are substantially different between in-text and front-page references. In addition, in-text referenced papers have a higher chance of being listed on the front-page of the same patent when they are moderately basic, less interdisciplinary, less novel, and have more scientific citations.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101564"},"PeriodicalIF":3.4,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1751157724000774/pdfft?md5=0f53abc32da83b95eefc022668120f82&pid=1-s2.0-S1751157724000774-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141728907","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}

{"title":"Investigating clinical links in edge-labeled citation networks of biomedical research: A translational science perspective","authors":"Xin Li ,&nbsp;Xuli Tang ,&nbsp;Wei Lu","doi":"10.1016/j.joi.2024.101558","DOIUrl":"10.1016/j.joi.2024.101558","url":null,"abstract":"<div><p>While clinical citations have been widely used as the preeminent measure of the clinical impact of biomedical paper, there has been a scarcity of in-depth studies exploring their temporal and structural characteristics, as well as its influence on the clinical translation. To fill this gap, we categorized biomedical papers and their citations into four groups from the translational science perspective: basic, clinical, mixed, and human-related. Subsequently, we constructed an edge-labeled citation network and four clinical translation networks. Our analysis encompassed 114,342 papers in the field of Alzheimer's Disease, accompanied by 5,161,626 citations, of which 2.77 % were clinical citations, 18.77 % basic citations, 41.85 % mixed citations, and 36.61 % human-related citations. First, utilizing time- and structure-randomized networks, we conducted a quantitative analysis of clinical citations' incidence patterns, impact assortativity, temporal occurrence patterns, and temporal co-location patterns throughout the lifecycles of biomedical research. Second, in comparison to control groups, we evaluated the short- and long-term impacts of different types of citations on the academic influence and clinical translation of biomedical research. Our findings reveal that clinical citations effectively bolster the academic influence of biomedical papers, and this positive effect appears to amplify over time. Conversely, while basic, mixed, and human-related citations may initially aid in the clinical translation of biomedical research, over 70 % of them exhibit an inhibitory effect on clinical translation in the long run. These findings afford us a deep and specific understanding of how clinical citations operate within the context of biomedical papers, thereby serving as a crucial guide for effectively promoting the clinical translation of biomedical research.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101558"},"PeriodicalIF":3.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141637243","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":"An approach for identifying complementary patents based on deep learning","authors":"Jinzhu Zhang,&nbsp;Jialu Shi,&nbsp;Peiyu Zhang","doi":"10.1016/j.joi.2024.101561","DOIUrl":"https://doi.org/10.1016/j.joi.2024.101561","url":null,"abstract":"<div><p>Current studies on technology mining and analysis often focus on patent similarity, with relatively limited research on patent complementarity. Specifically, the hierarchical relationships among patents are seldom used and a standardized complementary patents dataset has not been established. In addition, it is necessary to utilize both network structure features and text content features of patents, and find the most suitable representation learning method for them. Finally, the relationships among different dimensions of feature representations are complex, making it essential to learn the contributions of each dimension considering complex interactions. Therefore, this paper first constructs a complementary patents dataset using hierarchical relationships contained in IPC numbers. Secondly, we design three types of embedding methods for patent semantic representation, including network embedding, text embedding and fusion embedding. Thirdly, we propose a deep learning framework enhanced by the CBAM (Convolutional Block Attention Module) to deal with the complex interactions between different dimensions of patent representation. The result shows that the proposed method CompGCN combined with ESimCSE_Attention performs best for complementary patent identification and the F1 score reaches 95.76 %. In addition, HeGAN and ESimCSE_Attention are the most suitable embedding methods for network structure and text content respectively. These results not only validate the effectiveness of the proposed approach, but also provide helpful and useful suggestions for method selection and complex relationships mining.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101561"},"PeriodicalIF":3.4,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141606639","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}