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PurposeThis study aims to compare the impacts of proceedings papers in the fields of social science and humanities (SSH) and science.Design/methodology/approachThis study involved not only citations but also altmetric indexes to compare the impacts of proceedings papers among multiple disciplines with 1,779,166 records from Conference Proceedings Citation Index (CPCI) in the Web of Science (WoS) in the period of 2013–2017. The mean value, concentration ratio, Lorenz curves and correlation analysis are utilized into the comparative analysis.Findings(1) Proceedings papers in science fields had higher scholarly impacts than those in SSH fields. (2) As for societal impact, clinical, pre-clinical and health still ranked first, whereas physical science and engineering and technologies were transcended by SSH fields, which is different from the scholarly impact of proceedings papers. (3) As for proceedings papers, citations and altmetric indexes have weak or moderate correlations in all six fields, indicating that altmetrics can be supplemented when assessing proceedings papers.Originality/valueThis study is expected to enhance the understanding of proceedings papers and to promote accuracy of evaluation for them by exhibiting the multidisciplinary differences of their scholarly and societal impacts.

The correction practice of scientific publications is usually used to correct publication errors by issuing correction notices, and it is less explored compared to retraction practice. The aim of this study is to present an overview of correction practice and to explore the citation situation of correction notices in library and information science (LIS) journals, using 720 correction notices in the Web of Science from 2001–2020. Through bibliometrics and content analysis, we found the correcting rate of LIS was relatively low. The main types of corrected errors occurred in authorship, figure or table, references, etc. Most corrected errors were trivial or minor. The citation situation of correction notices was more complex than expected and could be classified into five types. It was relatively rare to cite both the corrected paper and correction notice in a standardized manner. The remaining four types of citation were unreasonable, which could influence citation practice and reduce the citations of corrected papers. We concluded that the appearance of the correction notice had affected the citation of the corrected papers to some extent, and researchers and databases needed to pay attention to this problem. We also provided some suggestions for improving correction practice.

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.

Scientific retraction practices are intended to help purge the continued use of flawed research and assist in maintaining the integrity, credibility and quality of scientific literature. However, the practical effect of retraction is still vague and needs to be further explored. In this study, we analysed the citation counts and sentiments (positive/negative) of retracted articles in psychology journals from Web of Science to explore the effect of retraction. Causal inference strategies were used to measure the net effect of retractions on citation. Results show that the retraction practices induced the citation counts to reduce as expected. However, the proportion of negative citations also decreased because of retraction, indicating an unsatisfied effect. The retraction practice of high-impact factors and open access journals was more effective than other journals. The study integrated an understanding of the dissemination of erroneous publications and provided implications for liabilities involved in the whole retraction process.

Purpose Library science and information science, two subdisciplines of library and information science (LIS), are developed independently but interconnectedly. In this information age, LIS is in a special period of transformation and development, which has caused some changes in both library science and information science. By accurately capturing these changes and analyzing them, the authors can effectively map the development of LIS in the new century, thus providing a reference for the evolution and development of the field. The purposes of this paper are to explore the mainstream research fields and frontiers of library science and information science, respectively, since the new century, and to make a comparative analysis of the two subdisciplines. Design/methodology/approach By using CiteSpace to visualize LIS journals, this study draws knowledge maps of the two subdisciplines of LIS through the co-occurrence descriptors network. Using burst detection algorithm, this study detects words of high frequency variation by investigating the time frequency distribution. Findings The results show that the research focus of library science has experienced a change from traditional to digital library while information science has moved from information to data focus. This study also finds the similarities and differences between mainstream areas of library science and information science. Originality/value This study focuses on the evolution of library science and information science, and explores their mainstream research fields and frontiers in the 21st century. These findings will promote the transformation and development of LIS as well as provide research directions for scholars in the field.

Scientific retraction helps purge the continued use of flawed research. However, the practical influence of it needs to be identified and quantified. In this study, we analyzed the citations of 46 psychological articles from Web of Science to explore the influence of retraction combining qualitative with quantitative methods. Our results show that 1) the overall rate of retraction was only 0.02% in psychology. The mean time from publication to retraction was much longer than that in medical fields; 2) retraction caused a significant decline in the post-retraction citations and triggered a series of changes in the lifecycle of citation; 3) some negative citations of retracted articles resulted from retraction, which occupied only a low percentage. The overall influence of retraction on citation was clear but remained to be improved.

As an increasing number of scientific literature dataset are open access, more attention has gravitated to keyword analysis in many scientific fields. Traditional keyword analyses include the frequency based and the network based methods, both providing efficient mining techniques for identifying the representative keywords. The semantic meanings behind the keywords are important for understanding the research content. However, traditional keyword analysis methods pay scant attention to semantic meanings; the network based or frequency based methods as traditionally used, present limited semantic associations among the keywords. Moreover, the ways in which the semantic meanings behind the keywords are associated to the citations are not clear. Thus, we use the Google Word2Vec model to build word vectors and reduce them to a two-dimensional plane in a Voronoi diagram using the t-SNE algorithm, to link meanings with citations. The distance between semantic meanings of keywords in two-dimensional plane are similar to distances in geographical space, thus we introduce a geographic metaphor, “Ghost City” to describe the relationship between semantics and citations for hot topics that have recently become not so hot. Along with “Ghost City” zones, “Always Hot”, “Newly Emerging Hot”, and “Always Silent” areas are classified and mapped, describing the spatial heterogeneity and homogeneity of the semantic distribution of keywords cited in a domain database. Using a collection of “geographical natural hazard” literature datasets, we demonstrate that the proposed method and classification scheme can efficiently provide a unique viewpoint for interpreting the interaction between semantics and the citations, as “Ghost City”, “Always Hot”, “Newly Emerging Hot”, and “Always Silent” areas.

In bibliometric research, keyword analysis of publications provides an effective way not only to investigate the knowledge structure of research domains, but also to explore the developing trends within domains. To identify the most representative keywords, many approaches have been proposed. Most of them focus on using statistical regularities, syntax, grammar, or network-based characteristics to select representative keywords for the domain analysis. In this paper, we argue that the domain knowledge is reflected by the semantic meanings behind keywords rather than the keywords themselves. We apply the Google Word2Vec model, a model of a word distribution using deep learning, to represent the semantic meanings of the keywords. Based on this work, we propose a new domain knowledge approach, the Semantic Frequency-Semantic Active Index, similar to Term Frequency-Inverse Document Frequency, to link domain and background information and identify infrequent but important keywords. We adopt a semantic similarity measuring process before statistical computation to compute the frequencies of “semantic units” rather than keyword frequencies. Semantic units are generated by word vector clustering, while the Inverse Document Frequency is extended to include the semantic inverse document frequency; thus only words in the inverse documents with a certain similarity will be counted. Taking geographical natural hazards as the domain and natural hazards as the background discipline, we identify the domain-specific knowledge that distinguishes geographical natural hazards from other types of natural hazards. We compare and discuss the advantages and disadvantages of the proposed method in relation to existing methods, finding that by introducing the semantic meaning of the keywords, our method supports more effective domain knowledge analysis.

Academic community evolution reveals the development of scientific collaboration among scientists. These social interactions of researchers can be well reflected by co-author network, making it feasible to investigate academic community through looking into co-author network, and to study community evolution through dynamic co-author network analysis. Existing metrics measure an author’s impact or centrality in co-author network individually, rather than considering the academic community as a whole. Besides, co-authors of a paper usually make different contributions reflected in the name order, which is often ignored in traditional co-author network analysis. Furthermore, attention has been paid mainly on those structure-level characteristics like the small-world coefficient and the clustering coefficient, the content-level characteristics like community, author, and topics, however, are crucial in the understanding of community evolution. To address those problems, we firstly propose a “comprehensive impact index” to evaluate the author in a co-author network by comprehensively considering the statistic-based impact and the network-based centrality. Then the comprehensive index value of all authors in a community is added up to evaluate the community as a whole. Further, a lifecycle strategy is proposed for the community evolution analysis. Taking geography academic community as a pilot study, we select 919 co-authored papers from the flagship journal of “Annals of the Association of American Geographers”. The co-author groups are generated by community detection method. Top three co-author groups are identified through computing with the proposed index and analyzed through the proposed lifecycle strategy from perspective of community structures, member authors, and impacts respectively. The results demonstrate our proposed index and strategy are more efficient for analyzing academic community evolution than traditional methods.

The author byline is an indispensable component of a scientific paper. Some journals have added contribution lists for each paper to provide detailed information of each author’s role. Many papers have explored, respectively, the byline and contribution lists. However, the relationship between the two remains unclear. We select three prominent general medical journals: Journal of the American Medical Association (JAMA), Annals of Internal Medicine (Annals), and PLOS Medicine (PLOS). We analyze the relationship between the author byline and contribution lists using four indexes. Four main findings emerged. First, the number, forms, and names of contribution lists significantly differed among the three journals, although they adopted the criteria of the International Committee of Medical Journal Editors. Second, a U-shaped relationship exists between the extent of contribution and author order: the participation levels in contribution lists were highest for first authors, followed by last and second authors, and then middle authors with the lowest levels. Third, regarding the consistency between author order in the contribution list and byline, every contribution category has a high consistency in JAMA and Annals, while PLOS shows a low consistency, in general. Fourth, the three journals have a similar distribution for the first authors in the contribution category; the first author in the byline contributes the highest proportion, followed by the middle and second authors, and then the last author with the lowest proportion. We also develop recommendations to modify academic and writing practice: implement structured cross-contribution lists, unify formats and standards of contribution lists, draft the author contribution criteria in the social sciences and humanities, and consider author contribution lists in scientific evaluation.