Search (24 results, page 1 of 2)

  • × author_ss:"Yan, E."
  1. Yan, E.: Finding knowledge paths among scientific disciplines (2014) 0.04 
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    Date
    26.10.2014 20:22:22
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.11, S.2331-2347
  2. Zheng, X.; Chen, J.; Yan, E.; Ni, C.: Gender and country biases in Wikipedia citations to scholarly publications (2023) 0.04 
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    Date
    22. 1.2023 18:53:32
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.2, S.219-233
  3. Yan, E.; Ding, Y.: Weighted citation : an indicator of an article's prestige (2010) 0.01 
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    Abstract
    The authors propose using the technique of weighted citation to measure an article's prestige. The technique allocates a different weight to each reference by taking into account the impact of citing journals and citation time intervals. Weightedcitation captures prestige, whereas citation counts capture popularity. They compare the value variances for popularity and prestige for articles published in the Journal of the American Society for Information Science and Technology from 1998 to 2007, and find that the majority have comparable status.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1635-1643
  4. Pan, X.; Yan, E.; Hua, W.: Science communication and dissemination in different cultures : an analysis of the audience for TED videos in China and abroad (2016) 0.01 
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    Abstract
    Disseminated across the world in more than 100 languages and viewed over 1 billion times, TED Talks is a successful example of web-based science communication. This study investigates the impact of TED Talks videos on YouKu, a Chinese video portal, and YouTube using 6 measures of impact: number of views; likes; dislikes; comments; bookmarks; and shares. In particular, we study the relationship between the topicality and impact of these videos. Findings demonstrate that topics vary greatly in terms of their impact: Topics on entertainment and psychology/philosophy receive more views and likes, whereas design/art and astronomy/biology/oceanography attract fewer comments and bookmarks. Moreover, we identify several topical differences between YouKu and YouTube users. Topics on global issues and technology are more popular on YouKu, whereas topics on entertainment and psychology/philosophy are more popular on YouTube. By analyzing the popularity distribution of videos and the audience characteristics of YouKu, we find that women are more interested in topics on education and psychology/philosophy, whereas men favor topics on technology and astronomy/biology/oceanography.
    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.6, S.1473-1486
  5. Ding, Y.; Jacob, E.K.; Fried, M.; Toma, I.; Yan, E.; Foo, S.; Milojevicacute, S.: Upper tag ontology for integrating social tagging data (2010) 0.01 
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    Abstract
    Data integration and mediation have become central concerns of information technology over the past few decades. With the advent of the Web and the rapid increases in the amount of data and the number of Web documents and users, researchers have focused on enhancing the interoperability of data through the development of metadata schemes. Other researchers have looked to the wealth of metadata generated by bookmarking sites on the Social Web. While several existing ontologies have capitalized on the semantics of metadata created by tagging activities, the Upper Tag Ontology (UTO) emphasizes the structure of tagging activities to facilitate modeling of tagging data and the integration of data from different bookmarking sites as well as the alignment of tagging ontologies. UTO is described and its utility in modeling, harvesting, integrating, searching, and analyzing data is demonstrated with metadata harvested from three major social tagging systems (Delicious, Flickr, and YouTube).
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.3, S.505-521
  6. Yan, E.; Yu, Q.: Using path-based approaches to examine the dynamic structure of discipline-level citation networks (2016) 0.01 
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    Abstract
    The objective of this paper is to identify the dynamic structure of several time-dependent, discipline-level citation networks through a path-based method. A network data set is prepared that comprises 27 subjects and their citations aggregated from more than 27,000 journals and proceedings indexed in the Scopus database. A maximum spanning tree method is employed to extract paths in the weighted, directed, and cyclic networks. This paper finds that subjects such as Medicine, Biochemistry, Chemistry, Materials Science, Physics, and Social Sciences are the ones with multiple branches in the spanning tree. This paper also finds that most paths connect science, technology, engineering, and mathematics (STEM) fields; 2 critical paths connecting STEM and non-STEM fields are the one from Mathematics to Decision Sciences and the one from Medicine to Social Sciences.
    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.8, S.1943-1955
  7. Yan, E.; Ding, Y.: Applying centrality measures to impact analysis : a coauthorship network analysis (2009) 0.01 
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    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.10, S.2107-2118
  8. Ding, Y.; Yan, E.; Frazho, A.; Caverlee, J.: PageRank for ranking authors in co-citation networks (2009) 0.01 
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    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.11, S.2229-2243
  9. Ding, Y.; Jacob, E.K.; Zhang, Z.; Foo, S.; Yan, E.; George, N.L.; Guo, L.: Perspectives on social tagging (2009) 0.01 
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    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.12, S.2388-2401
  10. Yan, E.; Ding, Y.; Sugimoto, C.R.: P-Rank: an indicator measuring prestige in heterogeneous scholarly networks (2011) 0.01 
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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.3, S.467-477
  11. Yan, E.; Sugimoto, C.R.: Institutional interactions : exploring social, cognitive, and geographic relationships between institutions as demonstrated through citation networks (2011) 0.01 
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    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.8, S.1498-1514
  12. Ding, 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 (2012) 0.01 
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    Journal of the American Society for Information Science and Technology. 63(2012) no.7, S.1313-1326
  13. Yan, E.: Research dynamics, impact, and dissemination : a topic-level analysis (2015) 0.01 
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    Journal of the Association for Information Science and Technology. 66(2015) no.11, S.2357-2372
  14. Hu, 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 (2015) 0.01 
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    Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2643-2656
  15. Yan, E.: Disciplinary knowledge production and diffusion in science (2016) 0.01 
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    Journal of the Association for Information Science and Technology. 67(2016) no.9, S.2223-2245
  16. Zhu, Y.; Yan, E.; Song, I.-Y..: ¬The use of a graph-based system to improve bibliographic information retrieval : system design, implementation, and evaluation (2017) 0.01 
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    Journal of the Association for Information Science and Technology. 68(2017) no.2, S.480-490
  17. Zhao, M.; Yan, E.; Li, K.: Data set mentions and citations : a content analysis of full-text publications (2018) 0.01 
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    Journal of the Association for Information Science and Technology. 69(2018) no.1, S.32-46
  18. Yan, E.; Chen, Z.; Li, K.: Authors' status and the perceived quality of their work : measuring citation sentiment change in nobel articles (2020) 0.01 
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    Journal of the Association for Information Science and Technology. 71(2020) no.3, S.314-324
  19. Wu, C.; Yan, E.; Zhu, Y.; Li, K.: Gender imbalance in the productivity of funded projects : a study of the outputs of National Institutes of Health R01 grants (2021) 0.01 
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    Journal of the Association for Information Science and Technology. 72(2021) no.11, S.1386-1399
  20. Milojevic, S.; Sugimoto, C.R.; Yan, E.; Ding, Y.: ¬The cognitive structure of Library and Information Science : analysis of article title words (2011) 0.01 
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    Journal of the American Society for Information Science and Technology. 62(2011) no.10, S.1933-1953

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