Search (17 results, page 1 of 1)

  • × author_ss:"Leydesdorff, L."
  1. Leydesdorff, L.: Can networks of journal-journal citations be used as indicators of change in the social sciences? (2003) 0.03 
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    Date
    10. 3.1997 20:31:13
    6.11.2005 19:02:22
  2. Leydesdorff, L.; Sun, Y.: National and international dimensions of the Triple Helix in Japan : university-industry-government versus international coauthorship relations (2009) 0.03 
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    Date
    22. 3.2009 19:07:20
  3. Leydesdorff, L.: ¬The construction and globalization of the knowledge base in inter-human communication systems (2003) 0.01 
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    Date
    22. 5.2003 19:48:04
  4. Leydesdorff, L.; Bornmann, L.; Wagner, C.S.: ¬The relative influences of government funding and international collaboration on citation impact (2019) 0.01 
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    Date
    8. 1.2019 18:22:45
  5. Leydesdorff, L.; Bornmann, L.: How fractional counting of citations affects the impact factor : normalization in terms of differences in citation potentials among fields of science (2011) 0.01 
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    Date
    22. 1.2011 12:51:07
  6. Hellsten, I.; Leydesdorff, L.: ¬The construction of interdisciplinarity : the development of the knowledge base and programmatic focus of the journal Climatic Change, 1977-2013 (2016) 0.01 
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    Date
    24. 8.2016 17:53:22
  7. Leydesdorff, L.; Johnson, M.W.; Ivanova, I.: Toward a calculus of redundancy : signification, codification, and anticipation in cultural evolution (2018) 0.01 
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    Date
    29. 9.2018 11:22:09
  8. Leydesdorff, L.: Clusters and maps of science journals based on bi-connected graphs in Journal Citation Reports (2004) 0.01 
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    Date
    10. 3.1997 20:31:13
  9. Egghe, L.; Leydesdorff, L.: ¬The relation between Pearson's correlation coefficient r and Salton's cosine measure (2009) 0.01 
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    Date
    18. 4.2009 20:54:30
  10. Leydesdorff, L.: How are new citation-based journal indicators adding to the bibliometric toolbox? (2009) 0.01 
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    Date
    19. 7.2009 12:23:20
  11. Leydesdorff, L.; Opthof, T.: Citation analysis with medical subject Headings (MeSH) using the Web of Knowledge : a new routine (2013) 0.01 
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    Date
    20. 4.2013 18:25:05
  12. Ye, F.Y.; Yu, S.S.; Leydesdorff, L.: ¬The Triple Helix of university-industry-government relations at the country level and its dynamic evolution under the pressures of globalization (2013) 0.01 
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    Date
    29.10.2013 20:28:15
  13. Leydesdorff, L.; Goldstone, R.L.: Interdisciplinarity at the journal and specialty level : the changing knowledge bases of the journal cognitive science (2014) 0.01 
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    Date
    26. 1.2014 20:36:28
  14. Leydesdorff, L.; Heimeriks, G.: ¬The self-organization of the European information society : the case of "biotechnology" (2001) 0.01 
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    Date
    11. 1.2003 20:01:15
  15. Leydesdorff, L.; Zhou, P.; Bornmann, L.: How can journal impact factors be normalized across fields of science? : An assessment in terms of percentile ranks and fractional counts (2013) 0.01 
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    Date
    27.12.2012 20:44:36
  16. Leydesdorff, L.; Ahrweiler, P.: In search of a network theory of innovations : relations, positions, and perspectives (2014) 0.01 
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    26.10.2014 20:25:43
  17. Hellsten, I.; Leydesdorff, L.: Automated analysis of actor-topic networks on twitter : new approaches to the analysis of socio-semantic networks (2020) 0.01 
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    Abstract
    Social media data provide increasing opportunities for the automated analysis of large sets of textual documents. So far, automated tools have been developed either to account for the social networks among participants in the debates, or to analyze the content of these debates. Less attention has been paid to mapping co-occurrences of actors (participants) and topics (content) in online debates that can be considered as socio-semantic networks. We propose a new, automated approach that uses the whole matrix of co-addressed topics and actors for understanding and visualizing online debates. We show the advantages of the new approach with the analysis of two data sets: first, a large set of English-language Twitter messages at the Rio?+?20 meeting, in June 2012 (72,077 tweets), and second, a smaller data set of Dutch-language Twitter messages on bird flu related to poultry farming in 2015-2017 (2,139 tweets). We discuss the theoretical, methodological, and substantive implications of our approach, also for the analysis of other social media data.

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