Search (2 results, page 1 of 1)

  • × year_i:[2020 TO 2030}
  • × author_ss:"Leydesdorff, L."
  1. Hellsten, I.; Leydesdorff, L.: Automated analysis of actor-topic networks on twitter : new approaches to the analysis of socio-semantic networks (2020) 0.00 
    0.0016913437 = product of:
  0.0033826875 = sum of:
    0.0033826875 = product of:
      0.006765375 = sum of:
        0.006765375 = weight(_text_:a in 5610) [ClassicSimilarity], result of:
          0.006765375 = score(doc=5610,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.12739488 = fieldWeight in 5610, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5610)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    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.
    Type
    a
  2. Leydesdorff, L.; Ivanova, I.: ¬The measurement of "interdisciplinarity" and "synergy" in scientific and extra-scientific collaborations (2021) 0.00 
    0.0014647468 = product of:
  0.0029294936 = sum of:
    0.0029294936 = product of:
      0.005858987 = sum of:
        0.005858987 = weight(_text_:a in 208) [ClassicSimilarity], result of:
          0.005858987 = score(doc=208,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.11032722 = fieldWeight in 208, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=208)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Problem solving often requires crossing boundaries, such as those between disciplines. When policy-makers call for "interdisciplinarity," however, they often mean "synergy." Synergy is generated when the whole offers more possibilities than the sum of its parts. An increase in the number of options above the sum of the options in subsets can be measured as redundancy; that is, the number of not-yet-realized options. The number of options available to an innovation system for realization can be as decisive for the system's survival as the historically already-realized innovations. Unlike "interdisciplinarity," "synergy" can also be generated in sectorial or geographical collaborations. The measurement of "synergy," however, requires a methodology different from the measurement of "interdisciplinarity." In this study, we discuss recent advances in the operationalization and measurement of "interdisciplinarity," and propose a methodology for measuring "synergy" based on information theory. The sharing of meanings attributed to information from different perspectives can increase redundancy. Increasing redundancy reduces the relative uncertainty, for example, in niches. The operationalization of the two concepts-"interdisciplinarity" and "synergy"-as different and partly overlapping indicators allows for distinguishing between the effects and the effectiveness of science-policy interventions in research priorities.
    Type
    a

Authors