Search (3 results, page 1 of 1)

  • × theme_ss:"Informetrie"
  • × theme_ss:"Internet"
  • × year_i:[2010 TO 2020}
  1. Yang, S.; Han, R.; Ding, J.; Song, Y.: ¬The distribution of Web citations (2012) 0.01 
    0.010175217 = product of:
  0.03052565 = sum of:
    0.03052565 = weight(_text_:based in 2735) [ClassicSimilarity], result of:
      0.03052565 = score(doc=2735,freq=2.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.19973516 = fieldWeight in 2735, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=2735)
  0.33333334 = coord(1/3)
    Abstract
    A substantial amount of research has focused on the persistence or availability of Web citations. The present study analyzes Web citation distributions. Web citations are defined as the mentions of the URLs of Web pages (Web resources) as references in academic papers. The present paper primarily focuses on the analysis of the URLs of Web citations and uses three sets of data, namely, Set 1 from the Humanities and Social Science Index in China (CSSCI, 1998-2009), Set 2 from the publications of two international computer science societies, Communications of the ACM and IEEE Computer (1995-1999), and Set 3 from the medical science database, MEDLINE, of the National Library of Medicine (1994-2006). Web citation distributions are investigated based on Web site types, Web page types, URL frequencies, URL depths, URL lengths, and year of article publication. Results show significant differences in the Web citation distributions among the three data sets. However, when the URLs of Web citations with the same hostnames are aggregated, the distributions in the three data sets are consistent with the power law (the Lotka function).
  2. Sugimoto, C.R.; Work, S.; Larivière, V.; Haustein, S.: Scholarly use of social media and altmetrics : A review of the literature (2017) 0.01 
    0.010175217 = product of:
  0.03052565 = sum of:
    0.03052565 = weight(_text_:based in 3781) [ClassicSimilarity], result of:
      0.03052565 = score(doc=3781,freq=2.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.19973516 = fieldWeight in 3781, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.046875 = fieldNorm(doc=3781)
  0.33333334 = coord(1/3)
    Abstract
    Social media has become integrated into the fabric of the scholarly communication system in fundamental ways, principally through scholarly use of social media platforms and the promotion of new indicators on the basis of interactions with these platforms. Research and scholarship in this area has accelerated since the coining and subsequent advocacy for altmetrics-that is, research indicators based on social media activity. This review provides an extensive account of the state-of-the art in both scholarly use of social media and altmetrics. The review consists of 2 main parts: the first examines the use of social media in academia, reviewing the various functions these platforms have in the scholarly communication process and the factors that affect this use. The second part reviews empirical studies of altmetrics, discussing the various interpretations of altmetrics, data collection and methodological limitations, and differences according to platform. The review ends with a critical discussion of the implications of this transformation in the scholarly communication system.
  3. Thelwall, M.: ¬A comparison of link and URL citation counting (2011) 0.01 
    0.008479347 = product of:
  0.025438042 = sum of:
    0.025438042 = weight(_text_:based in 4533) [ClassicSimilarity], result of:
      0.025438042 = score(doc=4533,freq=2.0), product of:
        0.15283063 = queryWeight, product of:
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.050723847 = queryNorm
        0.16644597 = fieldWeight in 4533, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0129938 = idf(docFreq=5906, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4533)
  0.33333334 = coord(1/3)
    Abstract
    Purpose - Link analysis is an established topic within webometrics. It normally uses counts of links between sets of web sites or to sets of web sites. These link counts are derived from web crawlers or commercial search engines with the latter being the only alternative for some investigations. This paper compares link counts with URL citation counts in order to assess whether the latter could be a replacement for the former if the major search engines withdraw their advanced hyperlink search facilities. Design/methodology/approach - URL citation counts are compared with link counts for a variety of data sets used in previous webometric studies. Findings - The results show a high degree of correlation between the two but with URL citations being much less numerous, at least outside academia and business. Research limitations/implications - The results cover a small selection of 15 case studies and so the findings are only indicative. Significant differences between results indicate that the difference between link counts and URL citation counts will vary between webometric studies. Practical implications - Should link searches be withdrawn, then link analyses of less well linked non-academic, non-commercial sites would be seriously weakened, although citations based on e-mail addresses could help to make citations more numerous than links for some business and academic contexts. Originality/value - This is the first systematic study of the difference between link counts and URL citation counts in a variety of contexts and it shows that there are significant differences between the two.

Authors