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: 23. Dezember 2017)
1Zhang, 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.
2Bu, 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.
3Li, 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.
4Tan, 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.
5Hu, 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
6Li, 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
7Song, 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.
8Li, 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.
9Ding, 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
10Ni, 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
11Zhang, 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
12Lin, 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
13Ding, Y. ; Yan, E.: Scholarly network similarities : how bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other.
In: Journal of the American Society for Information Science and Technology. 63(2012) no.7, S.1313-1326.
Abstract: This study explores the similarity among six types of scholarly networks aggregated at the institution level, including bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks. Cosine distance is chosen to measure the similarities among the six networks. The authors found that topical networks and coauthorship networks have the lowest similarity; cocitation networks and citation networks have high similarity; bibliographic coupling networks and cocitation networks have high similarity; and coword networks and topical networks have high similarity. In addition, through multidimensional scaling, two dimensions can be identified among the six networks: Dimension 1 can be interpreted as citation-based versus noncitation-based, and Dimension 2 can be interpreted as social versus cognitive. The authors recommend the use of hybrid or heterogeneous networks to study research interaction and scholarly communications.
14Sugimoto, C.R. ; Li, D. ; Russell, T.G. ; Finlay, S.C. ; Ding, Y.: ¬The shifting sands of disciplinary development : analyzing North American Library and Information Science dissertations using latent Dirichlet allocation.
In: Journal of the American Society for Information Science and Technology. 62(2011) no.1, S.185-204.
Abstract: This work identifies changes in dominant topics in library and information science (LIS) over time, by analyzing the 3,121 doctoral dissertations completed between 1930 and 2009 at North American Library and Information Science programs. The authors utilize latent Dirichlet allocation (LDA) to identify latent topics diachronically and to identify representative dissertations of those topics. The findings indicate that the main topics in LIS have changed substantially from those in the initial period (1930-1969) to the present (2000-2009). However, some themes occurred in multiple periods, representing core areas of the field: library history occurred in the first two periods; citation analysis in the second and third periods; and information-seeking behavior in the fourth and last period. Two topics occurred in three of the five periods: information retrieval and information use. One of the notable changes in the topics was the diminishing use of the word library (and related terms). This has implications for the provision of doctoral education in LIS. This work is compared to other earlier analyses and provides validation for the use of LDA in topic analysis of a discipline.
Wissenschaftsfach: Bibliothekswesen ; Informationswissenschaft
Behandelte Form: Dissertationen
15Ding, Y.: Applying weighted PageRank to author citation networks.
In: Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.236-245.
Abstract: This article aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information retrieval (IR) was selected as a test field and data from 1956-2008 were collected from Web of Science. Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal component analysis was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures
16Ding, Y.: Topic-based PageRank on author cocitation networks.
In: Journal of the American Society for Information Science and Technology. 62(2011) no.3, S.449-466.
Abstract: Ranking authors is vital for identifying a researcher's impact and standing within a scientific field. There are many different ranking methods (e.g., citations, publications, h-index, PageRank, and weighted PageRank), but most of them are topic-independent. This paper proposes topic-dependent ranks based on the combination of a topic model and a weighted PageRank algorithm. The author-conference-topic (ACT) model was used to extract topic distribution of individual authors. Two ways for combining the ACT model with the PageRank algorithm are proposed: simple combination (I_PR) or using a topic distribution as a weighted vector for PageRank (PR_t). Information retrieval was chosen as the test field and representative authors for different topics at different time phases were identified. Principal component analysis (PCA) was applied to analyze the ranking difference between I_PR and PR_t.
17Yan, E. ; Ding, Y. ; Sugimoto, C.R.: P-Rank: an indicator measuring prestige in heterogeneous scholarly networks.
In: Journal of the American Society for Information Science and Technology. 62(2011) no.3, S.467-477.
Abstract: Ranking scientific productivity and prestige are often limited to homogeneous networks. These networks are unable to account for the multiple factors that constitute the scholarly communication and reward system. This study proposes a new informetric indicator, P-Rank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P-Rank differentiates the weight of each citation based on its citing papers, citing journals, and citing authors. Articles from 16 representative library and information science journals are selected as the dataset. Principle Component Analysis is conducted to examine the relationship between P-Rank and other bibliometric indicators. We also compare the correlation and rank variances between citation counts and P-Rank scores. This work provides a new approach to examining prestige in scholarly communication networks in a more comprehensive and nuanced way.
18He, B. ; Ding, Y. ; Ni, C.: Mining enriched contextual information of scientific collaboration : a meso perspective.
In: Journal of the American Society for Information Science and Technology. 62(2011) no.5, S.831-845.
Abstract: Studying scientific collaboration using coauthorship networks has attracted much attention in recent years. How and in what context two authors collaborate remain among the major questions. Previous studies, however, have focused on either exploring the global topology of coauthorship networks (macro perspective) or ranking the impact of individual authors (micro perspective). Neither of them has provided information on the context of the collaboration between two specific authors, which may potentially imply rich socioeconomic, disciplinary, and institutional information on collaboration. Different from the macro perspective and micro perspective, this article proposes a novel method (meso perspective) to analyze scientific collaboration, in which a contextual subgraph is extracted as the unit of analysis. A contextual subgraph is defined as a small subgraph of a large-scale coauthorship network that captures relationship and context between two coauthors. This method is applied to the field of library and information science. Topological properties of all the subgraphs in four time spans are investigated, including size, average degree, clustering coefficient, and network centralization. Results show that contextual subgprahs capture useful contextual information on two authors' collaboration.
19Milojevic, S. ; Sugimoto, C.R. ; Yan, E. ; Ding, Y.: ¬The cognitive structure of Library and Information Science : analysis of article title words.
In: Journal of the American Society for Information Science and Technology. 62(2011) no.10, S.1933-1953.
Abstract: This study comprises a suite of analyses of words in article titles in order to reveal the cognitive structure of Library and Information Science (LIS). The use of title words to elucidate the cognitive structure of LIS has been relatively neglected. The present study addresses this gap by performing (a) co-word analysis and hierarchical clustering, (b) multidimensional scaling, and (c) determination of trends in usage of terms. The study is based on 10,344 articles published between 1988 and 2007 in 16 LIS journals. Methodologically, novel aspects of this study are: (a) its large scale, (b) removal of non-specific title words based on the "word concentration" measure (c) identification of the most frequent terms that include both single words and phrases, and (d) presentation of the relative frequencies of terms using "heatmaps". Conceptually, our analysis reveals that LIS consists of three main branches: the traditionally recognized library-related and information-related branches, plus an equally distinct bibliometrics/scientometrics branch. The three branches focus on: libraries, information, and science, respectively. In addition, our study identifies substructures within each branch. We also tentatively identify "information seeking behavior" as a branch that is establishing itself separate from the three main branches. Furthermore, we find that cognitive concepts in LIS evolve continuously, with no stasis since 1992. The most rapid development occurred between 1998 and 2001, influenced by the increased focus on the Internet. The change in the cognitive landscape is found to be driven by the emergence of new information technologies, and the retirement of old ones.
Wissenschaftsfach: Bibliothekswesen ; Informationswissenschaft
20Li, D. ; Ding, Y. ; Sugimoto, C. ; He, B. ; Tang, J. ; Yan, E. ; Lin, N. ; Qin, Z. ; Dong, T.: Modeling topic and community structure in social tagging : the TTR-LDA-Community model.
In: Journal of the American Society for Information Science and Technology. 62(2011) no.9, S.1849-1866.
Abstract: The presence of social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using social tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems.
Themenfeld: Social tagging