Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 04. Juni 2021)
1Zhai, Y. ; Ding, Y. ; Zhang, H.: Innovation adoption : broadcasting versus virality.
In: Journal of the Association for Information Science and Technology. 72(2021) no.4, S.403-416.
Abstract: Diffusion channels are critical to determining the adoption scale, which leads to the ultimate impact of an innovation. The aim of this study is to develop an integrative understanding of the impact of two diffusion channels (i.e., broadcasting vs. virality) on innovation adoption. Using citations of a series of classic algorithms and the time series of co-authorship as the footprints of their diffusion trajectories, we propose a novel method to analyze the intertwining relationships between broadcasting and virality in the innovation diffusion process. Our findings show that broadcasting and virality have similar diffusion power, but play different roles across diffusion stages. Broadcasting is more powerful in the early stages but may be gradually caught up or even surpassed by virality in the later period. Meanwhile, diffusion speed in virality is significantly faster than broadcasting and members from virality channels tend to adopt the same innovation repetitively.
Inhalt: Vgl.: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24420.
2Lu, C. ; Zhang, Y. ; Ahn, Y.-Y. ; Ding, Y. ; Zhang, C. ; Ma, D.: Co-contributorship network and division of labor in individual scientific collaborations.
In: Journal of the Association for Information Science and Technology. 71(2020) no.10, S.1162-1178.
Abstract: Collaborations are pervasive in current science. Collaborations have been studied and encouraged in many disciplines. However, little is known about how a team really functions from the detailed division of labor within. In this research, we investigate the patterns of scientific collaboration and division of labor within individual scholarly articles by analyzing their co-contributorship networks. Co-contributorship networks are constructed by performing the one-mode projection of the author-task bipartite networks obtained from 138,787 articles published in PLoS journals. Given an article, we define 3 types of contributors: Specialists, Team-players, and Versatiles. Specialists are those who contribute to all their tasks alone; team-players are those who contribute to every task with other collaborators; and versatiles are those who do both. We find that team-players are the majority and they tend to contribute to the 5 most common tasks as expected, such as "data analysis" and "performing experiments." The specialists and versatiles are more prevalent than expected by our designed 2 null models. Versatiles tend to be senior authors associated with funding and supervision. Specialists are associated with 2 contrasting roles: the supervising role as team leaders or marginal and specialized contributors.
3Lu, C. ; Bu, Y. ; Wang, J. ; Ding, Y. ; Torvik, V. ; Schnaars, M. ; Zhang, C.: Examining scientific writing styles from the perspective of linguistic complexity : a cross-level moderation model.
In: Journal of the Association for Information Science and Technology. 70(2019) no.5, S.462-475.
Abstract: Publishing articles in high-impact English journals is difficult for scholars around the world, especially for non-native English-speaking scholars (NNESs), most of whom struggle with proficiency in English. To uncover the differences in English scientific writing between native English-speaking scholars (NESs) and NNESs, we collected a large-scale data set containing more than 150,000 full-text articles published in PLoS between 2006 and 2015. We divided these articles into three groups according to the ethnic backgrounds of the first and corresponding authors, obtained by Ethnea, and examined the scientific writing styles in English from a two-fold perspective of linguistic complexity: (a) syntactic complexity, including measurements of sentence length and sentence complexity; and (b) lexical complexity, including measurements of lexical diversity, lexical density, and lexical sophistication. The observations suggest marginal differences between groups in syntactical and lexical complexity.
Inhalt: Vgl.: https://onlinelibrary.wiley.com/doi/10.1002/asi.24126.
4Li, D. ; Wang, Y. ; Madden, A. ; Ding, Y. ; Sun, G.G. ; Zhang, N. ; Zhou, E.: Analyzing stock market trends using social media user moods and social influence.
In: Journal of the Association for Information Science and Technology. 70(2019) no.9, S.1000-1013.
Abstract: Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain some of the irrational behaviors associated with financial decisions to help explain some of the fluctuations. In this study we argue that social media users who demonstrate an interest in finance can offer insights into ways in which irrational behaviors may affect a stock market. To test this, we analyzed all the data collected over a 3-month period in 2011 from Tencent Weibo (one of the largest microblogging websites in China). We designed a social influence (SI)-based Tencent finance-related moods model to simulate investors' irrational behaviors, and designed a Tencent Moods-based Stock Trend Analysis (TM_STA) model to detect correlations between Tencent moods and the Hushen-300 index (one of the most important financial indexes in China). Experimental results show that the proposed method can help explain the data fluctuation. The findings support the existing behavioral financial theory, and can help to understand short-term rises and falls in a stock market. We use behavioral financial theory to further explain our findings, and to propose a trading model to verify the proposed model.
Inhalt: Vgl.: https://onlinelibrary.wiley.com/doi/10.1002/asi.24173.
5Huang, Y. ; Bu, Y. ; Ding, Y. ; Lu, W.: From zero to one : a perspective on citing.
In: Journal of the Association for Information Science and Technology. 70(2019) no.10, S.1098-1107.
Abstract: This article investigates the lengths of time that publications with different numbers of citations take to receive their first citation (the beginning stage), and then compares the lengths of time to receive two or more citations after receiving the first citation (the accumulative stage) in the field of computer science. We find that in the beginning stage, that is, from zero to one citation, high-, medium-, and low-cited publications do not obviously exhibit different lengths of time. However, in the accumulative stage, that is, from one to N citations, highly cited publications begin to receive citations much more rapidly than medium- and low-cited publications. Moreover, as N increases, the difference in receiving new citations among high-, medium-, and low-cited publications increases quite significantly.
Inhalt: Vgl.: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24177.
6Zhang, C. ; Bu, Y. ; Ding, Y. ; Xu, J.: Understanding scientific collaboration : homophily, transitivity, and preferential attachment.
In: Journal of the Association for Information Science and Technology. 69(2018) no.1, S.72-86.
Abstract: Scientific collaboration is essential in solving problems and breeding innovation. Coauthor network analysis has been utilized to study scholars' collaborations for a long time, but these studies have not simultaneously taken different collaboration features into consideration. In this paper, we present a systematic approach to analyze the differences in possibilities that two authors will cooperate as seen from the effects of homophily, transitivity, and preferential attachment. Exponential random graph models (ERGMs) are applied in this research. We find that different types of publications one author has written play diverse roles in his/her collaborations. An author's tendency to form new collaborations with her/his coauthors' collaborators is strong, where the more coauthors one author had before, the more new collaborators he/she will attract. We demonstrate that considering the authors' attributes and homophily effects as well as the transitivity and preferential attachment effects of the coauthorship network in which they are embedded helps us gain a comprehensive understanding of scientific collaboration.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23916/full.
7Bu, Y. ; Ding, Y. ; Xu, J. ; Liang, X. ; Gao, G. ; Zhao, Y.: Understanding success through the diversity of collaborators and the milestone of career.
In: Journal of the Association for Information Science and Technology. 69(2018) no.1, S.87-97.
Abstract: Scientific collaboration is vital to many fields, and it is common to see scholars seek out experienced researchers or experts in a domain with whom they can share knowledge, experience, and resources. To explore the diversity of research collaborations, this article performs a temporal analysis on the scientific careers of researchers in the field of computer science. Specifically, we analyze collaborators using 2 indicators: the research topic diversity, measured by the Author-Conference-Topic model and cosine, and the impact diversity, measured by the normalized standard deviation of h-indices. We find that the collaborators of high-impact researchers tend to study diverse research topics and have diverse h-indices. Moreover, by setting PhD graduation as an important milestone in researchers' careers, we examine several indicators related to scientific collaboration and their effects on a career. The results show that collaborating with authoritative authors plays an important role prior to a researcher's PhD graduation, but working with non-authoritative authors carries more weight after PhD graduation.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23911/full.
8Zhai, Y ; Ding, Y. ; Wang, F.: Measuring the diffusion of an innovation : a citation analysis.
In: Journal of the Association for Information Science and Technology. 69(2018) no.3, S.368-379.
Abstract: Innovations transform our research traditions and become the driving force to advance individual, group, and social creativity. Meanwhile, interdisciplinary research is increasingly being promoted as a route to advance the complex challenges we face as a society. In this paper, we use Latent Dirichlet Allocation (LDA) citation as a proxy context for the diffusion of an innovation. With an analysis of topic evolution, we divide the diffusion process into five stages: testing and evaluation, implementation, improvement, extending, and fading. Through a correlation analysis of topic and subject, we show the application of LDA in different subjects. We also reveal the cross-boundary diffusion between different subjects based on the analysis of the interdisciplinary studies. The results show that as LDA is transferred into different areas, the adoption of each subject is relatively adjacent to those with similar research interests. Our findings further support researchers' understanding of the impact formation of innovation.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23898/full.
9Bu, Y. ; Ding, Y. ; Liang, X. ; Murray, D.S.: Understanding persistent scientific collaboration.
In: Journal of the Association for Information Science and Technology. 69(2018) no.3, S.438-448.
Abstract: Common sense suggests that persistence is key to success. In academia, successful researchers have been found more likely to be persistent in publishing, but little attention has been given to how persistence in maintaining collaborative relationships affects career success. This paper proposes a new bibliometric understanding of persistence that considers the prominent role of collaboration in contemporary science. Using this perspective, we analyze the relationship between persistent collaboration and publication quality along several dimensions: degree of transdisciplinarity, difference in coauthor's scientific age and their scientific impact, and research-team size. Contrary to traditional wisdom, our results show that persistent scientific collaboration does not always result in high-quality papers. We find that the most persistent transdisciplinary collaboration tends to output high-impact publications, and that those coauthors with diverse scientific impact or scientific ages benefit from persistent collaboration more than homogeneous compositions. We also find that researchers persistently working in large groups tend to publish lower-impact papers. These results contradict the colloquial understanding of collaboration in academia and paint a more nuanced picture of how persistent scientific collaboration relates to success, a picture that can provide valuable insights to researchers, funding agencies, policy makers, and mentor-mentee program directors. Moreover, the methodology in this study showcases a feasible approach to measure persistent collaboration.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23966/full.
10Min, C. ; Ding, Y. ; Li, J. ; Bu, Y. ; Pei, L. ; Sun, J.: Innovation or imitation : the diffusion of citations.
In: Journal of the Association for Information Science and Technology. 69(2018) no.10, S.1271-1282.
Abstract: Citations in scientific literature are important both for tracking the historical development of scientific ideas and for forecasting research trends. However, the diffusion mechanisms underlying the citation process remain poorly understood, despite the frequent and longstanding use of citation counts for assessment purposes within the scientific community. Here, we extend the study of citation dynamics to a more general diffusion process to understand how citation growth associates with different diffusion patterns. Using a classic diffusion model, we quantify and illustrate specific diffusion mechanisms which have been proven to exert a significant impact on the growth and decay of citation counts. Experiments reveal a positive relation between the "low p and low q" pattern and high scientific impact. A sharp citation peak produced by rapid change of citation counts, however, has a negative effect on future impact. In addition, we have suggested a simple indicator, saturation level, to roughly estimate an individual article's current stage in the life cycle and its potential to attract future attention. The proposed approach can also be extended to higher levels of aggregation (e.g., individual scientists, journals, institutions), providing further insights into the practice of scientific evaluation.
Inhalt: Vgl.: https://onlinelibrary.wiley.com/doi/10.1002/asi.24047.
11Li, D. ; Luo, Z. ; Ding, Y. ; Tang, J. ; Sun, G.G.-Z. ; Dai, X. ; Du, J. ; Zhang, J. ; Kong, S.: User-level microblogging recommendation incorporating social influence.
In: Journal of the Association for Information Science and Technology. 68(2017) no.3, S.553-568.
Abstract: With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23681/full.
12Tan, L.K.-W. ; Na, J.-C. ; Ding, Y.: Influence diffusion detection using the influence style (INFUSE) model.
In: Journal of the Association for Information Science and Technology. 66(2015) no.8, S.1717-1733.
Abstract: Blogs are readily available sources of opinions and sentiments that in turn could influence the opinions of the blog readers. Previous studies have attempted to infer influence from blog features, but they have ignored the possible influence styles that describe the different ways in which influence is exerted. We propose a novel approach to analyzing bloggers' influence styles and using the influence styles as features to improve the performance of influence diffusion detection among linked bloggers. The proposed influence style (INFUSE) model describes bloggers' influence through their engagement style, persuasion style, and persona. Methods used include similarity analysis to detect the creating-sharing aspect of engagement style, subjectivity analysis to measure persuasion style, and sentiment analysis to identify persona style. We further extend the INFUSE model to detect influence diffusion among linked bloggers based on the bloggers' influence styles. The INFUSE model performed well with an average F1 score of 76% compared with the in-degree and sentiment-value baseline approaches. Previous studies have focused on the existence of influence among linked bloggers in detecting influence diffusion, but our INFUSE model is shown to provide a fine-grained description of the manner in which influence is diffused based on the bloggers' influence styles.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23287/abstract.
13Hu, B. ; Dong, X. ; Zhang, C. ; Bowman, T.D. ; Ding, Y. ; Milojevic, S. ; Ni, C. ; Yan, E. ; Larivière, V.: ¬A lead-lag analysis of the topic evolution patterns for preprints and publications.
In: Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2643-2656.
Abstract: This study applied LDA (latent Dirichlet allocation) and regression analysis to conduct a lead-lag analysis to identify different topic evolution patterns between preprints and papers from arXiv and the Web of Science (WoS) in astrophysics over the last 20 years (1992-2011). Fifty topics in arXiv and WoS were generated using an LDA algorithm and then regression models were used to explain 4 types of topic growth patterns. Based on the slopes of the fitted equation curves, the paper redefines the topic trends and popularity. Results show that arXiv and WoS share similar topics in a given domain, but differ in evolution trends. Topics in WoS lose their popularity much earlier and their durations of popularity are shorter than those in arXiv. This work demonstrates that open access preprints have stronger growth tendency as compared to traditional printed publications.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23347/abstract.
Themenfeld: Elektronisches Publizieren
14Li, D. ; Tang, J. ; Ding, Y. ; Shuai, X. ; Chambers, T. ; Sun, G. ; Luo, Z. ; Zhang, J.: Topic-level opinion influence model (TOIM) : an investigation using tencent microblogging.
In: Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2657-2673.
Abstract: Text mining has been widely used in multiple types of user-generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic-level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recall and f1-measure. Finally, we demonstrate some useful applications of TOIM in analyzing users' behaviors in Tencent Weibo.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23350/abstract.
Themenfeld: Data Mining
15Song, M. ; Kim, S.Y. ; Zhang, G. ; Ding, Y. ; Chambers, T.: Productivity and influence in bioinformatics : a bibliometric analysis using PubMed central.
In: Journal of the Association for Information Science and Technology. 65(2014) no.2, S.352-371.
Abstract: Bioinformatics is a fast-growing field based on the optimal use of "big data" gathered in genomic, proteomics, and functional genomics research. In this paper, we conduct a comprehensive and in-depth bibliometric analysis of the field of bioinformatics by extracting citation data from PubMed Central full-text. Citation data for the period 2000 to 2011, comprising 20,869 papers with 546,245 citations, was used to evaluate the productivity and influence of this emerging field. Four measures were used to identify productivity; most productive authors, most productive countries, most productive organizations, and most popular subject terms. Research impact was analyzed based on the measures of most cited papers, most cited authors, emerging stars, and leading organizations. Results show the overall trends between the periods 2000 to 2003 and 2004 to 2007 were dissimilar, while trends between the periods 2004 to 2007 and 2008 to 2011 were similar. In addition, the field of bioinformatics has undergone a significant shift, co-evolving with other biomedical disciplines.
16Li, R. ; Chambers, T. ; Ding, Y. ; Zhang, G. ; Meng, L.: Patent citation analysis : calculating science linkage based on citing motivation.
In: Journal of the Association for Information Science and Technology. 65(2014) no.5, S.1007-1017.
Abstract: Science linkage is a widely used patent bibliometric indicator to measure patent linkage to scientific research based on the frequency of citations to scientific papers within the patent. Science linkage is also regarded as noisy because the subject of patent citation behavior varies from inventors/applicants to examiners. In order to identify and ultimately reduce this noise, we analyzed the different citing motivations of examiners and inventors/applicants. We built 4 hypotheses based upon our study of patent law, the unique economic nature of a patent, and a patent citation's market effect. To test our hypotheses, we conducted an expert survey based on our science linkage calculation in the domain of catalyst from U.S. patent data (2006-2009) over 3 types of citations: self-citation by inventor/applicant, non-self-citation by inventor/applicant, and citation by examiner. According to our results, evaluated by domain experts, we conclude that the non-self-citation by inventor/applicant is quite noisy and cannot indicate science linkage and that self-citation by inventor/applicant, although limited, is more appropriate for understanding science linkage.
17Ding, Y. ; Zhang, G. ; Chambers, T. ; Song, M. ; Wang, X. ; Zhai, C.: Content-based citation analysis : the next generation of citation analysis.
In: Journal of the Association for Information Science and Technology. 65(2014) no.9, S.1820-1833.
Abstract: Traditional citation analysis has been widely applied to detect patterns of scientific collaboration, map the landscapes of scholarly disciplines, assess the impact of research outputs, and observe knowledge transfer across domains. It is, however, limited, as it assumes all citations are of similar value and weights each equally. Content-based citation analysis (CCA) addresses a citation's value by interpreting each one based on its context at both the syntactic and semantic levels. This paper provides a comprehensive overview of CAA research in terms of its theoretical foundations, methodical approaches, and example applications. In addition, we highlight how increased computational capabilities and publicly available full-text resources have opened this area of research to vast possibilities, which enable deeper citation analysis, more accurate citation prediction, and increased knowledge discovery.
Themenfeld: Citation indexing
18Ni, C. ; Shaw, D. ; Lind, S.M. ; Ding, Y.: Journal impact and proximity : an assessment using bibliographic features.
In: Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.802-817.
Abstract: Journals in the Information Science & Library Science category of Journal Citation Reports (JCR) were compared using both bibliometric and bibliographic features. Data collected covered journal impact factor (JIF), number of issues per year, number of authors per article, longevity, editorial board membership, frequency of publication, number of databases indexing the journal, number of aggregators providing full-text access, country of publication, JCR categories, Dewey decimal classification, and journal statement of scope. Three features significantly correlated with JIF: number of editorial board members and number of JCR categories in which a journal is listed correlated positively; journal longevity correlated negatively with JIF. Coword analysis of journal descriptions provided a proximity clustering of journals, which differed considerably from the clusters based on editorial board membership. Finally, a multiple linear regression model was built to predict the JIF based on all the collected bibliographic features.
Wissenschaftsfach: Bibliothekswesen ; Informationswissenschaft
19Zhang, G. ; Ding, Y. ; Milojevic, S.: Citation content analysis (CCA) : a framework for syntactic and semantic analysis of citation content.
In: Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1490-1503.
Abstract: This study proposes a new framework for citation content analysis (CCA), for syntactic and semantic analysis of citation content that can be used to better analyze the rich sociocultural context of research behavior. This framework could be considered the next generation of citation analysis. The authors briefly review the history and features of content analysis in traditional social sciences and its previous application in library and information science (LIS). Based on critical discussion of the theoretical necessity of a new method as well as the limits of citation analysis, the nature and purposes of CCA are discussed, and potential procedures to conduct CCA, including principles to identify the reference scope, a two-dimensional (citing and cited) and two-module (syntactic and semantic) codebook, are provided and described. Future work and implications are also suggested.
Themenfeld: Citation indexing
20Lin, N. ; Li, D. ; Ding, Y. ; He, B. ; Qin, Z. ; Tang, J. ; Li, J. ; Dong, T.: ¬The dynamic features of Delicious, Flickr, and YouTube.
In: Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.139-162.
Abstract: This article investigates the dynamic features of social tagging vocabularies in Delicious, Flickr, and YouTube from 2003 to 2008. Three algorithms are designed to study the macro- and micro-tag growth as well as the dynamics of taggers' activities, respectively. Moreover, we propose a Tagger Tag Resource Latent Dirichlet Allocation (TTR-LDA) model to explore the evolution of topics emerging from those social vocabularies. Our results show that (a) at the macro level, tag growth in all the three tagging systems obeys power law distribution with exponents lower than 1; at the micro level, the tag growth of popular resources in all three tagging systems follows a similar power law distribution; (b) the exponents of tag growth vary in different evolving stages of resources; (c) the growth of number of taggers associated with different popular resources presents a feature of convergence over time; (d) the active level of taggers has a positive correlation with the macro-tag growth of different tagging systems; and (e) some topics evolve into several subtopics over time while others experience relatively stable stages in which their contents do not change much, and certain groups of taggers continue their interests in them.
Themenfeld: Social tagging
Objekt: Delicious ; Flickr ; YouTube