Search (116 results, page 6 of 6)

  • × author_ss:"Thelwall, M."
  • × language_ss:"e"
  1. Barjak, F.; Li, X.; Thelwall, M.: Which factors explain the Web impact of scientists' personal homepages? (2007) 0.00
    0.0039324276 = product of:
      0.009831069 = sum of:
        0.0066735395 = weight(_text_:a in 73) [ClassicSimilarity], result of:
          0.0066735395 = score(doc=73,freq=12.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.12482099 = fieldWeight in 73, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=73)
        0.003157529 = product of:
          0.006315058 = sum of:
            0.006315058 = weight(_text_:information in 73) [ClassicSimilarity], result of:
              0.006315058 = score(doc=73,freq=2.0), product of:
                0.08139861 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046368346 = queryNorm
                0.0775819 = fieldWeight in 73, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.03125 = fieldNorm(doc=73)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    In recent years, a considerable body of Webometric research has used hyperlinks to generate indicators for the impact of Web documents and the organizations that created them. The relationship between this Web impact and other, offline impact indicators has been explored for entire universities, departments, countries, and scientific journals, but not yet for individual scientists-an important omission. The present research closes this gap by investigating factors that may influence the Web impact (i.e., inlink counts) of scientists' personal homepages. Data concerning 456 scientists from five scientific disciplines in six European countries were analyzed, showing that both homepage content and personal and institutional characteristics of the homepage owners had significant relationships with inlink counts. A multivariate statistical analysis confirmed that full-text articles are the most linked-to content in homepages. At the individual homepage level, hyperlinks are related to several offline characteristics. Notable differences regarding total inlinks to scientists' homepages exist between the scientific disciplines and the countries in the sample. There also are both gender and age effects: fewer external inlinks (i.e., links from other Web domains) to the homepages of female and of older scientists. There is only a weak relationship between a scientist's recognition and homepage inlinks and, surprisingly, no relationship between research productivity and inlink counts. Contrary to expectations, the size of collaboration networks is negatively related to hyperlink counts. Some of the relationships between hyperlinks to homepages and the properties of their owners can be explained by the content that the homepage owners put on their homepage and their level of Internet use; however, the findings about productivity and collaborations do not seem to have a simple, intuitive explanation. Overall, the results emphasize the complexity of the phenomenon of Web linking, when analyzed at the level of individual pages.
    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.2, S.200-211
    Type
    a
  2. Mohammadi, E.; Thelwall, M.; Haustein, S.; Larivière, V.: Who reads research articles? : an altmetrics analysis of Mendeley user categories (2015) 0.00
    0.003594941 = product of:
      0.008987352 = sum of:
        0.0034055763 = weight(_text_:a in 2162) [ClassicSimilarity], result of:
          0.0034055763 = score(doc=2162,freq=2.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.06369744 = fieldWeight in 2162, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2162)
        0.0055817757 = product of:
          0.011163551 = sum of:
            0.011163551 = weight(_text_:information in 2162) [ClassicSimilarity], result of:
              0.011163551 = score(doc=2162,freq=4.0), product of:
                0.08139861 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046368346 = queryNorm
                0.13714671 = fieldWeight in 2162, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2162)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    Little detailed information is known about who reads research articles and the contexts in which research articles are read. Using data about people who register in Mendeley as readers of articles, this article explores different types of users of Clinical Medicine, Engineering and Technology, Social Science, Physics, and Chemistry articles inside and outside academia. The majority of readers for all disciplines were PhD students, postgraduates, and postdocs but other types of academics were also represented. In addition, many Clinical Medicine articles were read by medical professionals. The highest correlations between citations and Mendeley readership counts were found for types of users who often authored academic articles, except for associate professors in some sub-disciplines. This suggests that Mendeley readership can reflect usage similar to traditional citation impact if the data are restricted to readers who are also authors without the delay of impact measured by citation counts. At the same time, Mendeley statistics can also reveal the hidden impact of some research articles, such as educational value for nonauthor users inside academia or the impact of research articles on practice for readers outside academia.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1832-1846
    Type
    a
  3. Thelwall, M.; Wilkinson, D.: Public dialogs in social network sites : What is their purpose? (2010) 0.00
    0.003529194 = product of:
      0.008822985 = sum of:
        0.004086692 = weight(_text_:a in 3327) [ClassicSimilarity], result of:
          0.004086692 = score(doc=3327,freq=2.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.07643694 = fieldWeight in 3327, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=3327)
        0.0047362936 = product of:
          0.009472587 = sum of:
            0.009472587 = weight(_text_:information in 3327) [ClassicSimilarity], result of:
              0.009472587 = score(doc=3327,freq=2.0), product of:
                0.08139861 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046368346 = queryNorm
                0.116372846 = fieldWeight in 3327, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3327)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.2, S.392-404
    Type
    a
  4. Thelwall, M.; Buckley, K.: Topic-based sentiment analysis for the social web : the role of mood and issue-related words (2013) 0.00
    0.003529194 = product of:
      0.008822985 = sum of:
        0.004086692 = weight(_text_:a in 1004) [ClassicSimilarity], result of:
          0.004086692 = score(doc=1004,freq=2.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.07643694 = fieldWeight in 1004, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=1004)
        0.0047362936 = product of:
          0.009472587 = sum of:
            0.009472587 = weight(_text_:information in 1004) [ClassicSimilarity], result of:
              0.009472587 = score(doc=1004,freq=2.0), product of:
                0.08139861 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046368346 = queryNorm
                0.116372846 = fieldWeight in 1004, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046875 = fieldNorm(doc=1004)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.8, S.1608-1617
    Type
    a
  5. Thelwall, M.; Foster, D.: Male or female gender-polarized YouTube videos are less viewed (2021) 0.00
    0.003529194 = product of:
      0.008822985 = sum of:
        0.004086692 = weight(_text_:a in 414) [ClassicSimilarity], result of:
          0.004086692 = score(doc=414,freq=2.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.07643694 = fieldWeight in 414, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=414)
        0.0047362936 = product of:
          0.009472587 = sum of:
            0.009472587 = weight(_text_:information in 414) [ClassicSimilarity], result of:
              0.009472587 = score(doc=414,freq=2.0), product of:
                0.08139861 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046368346 = queryNorm
                0.116372846 = fieldWeight in 414, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046875 = fieldNorm(doc=414)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.12, S.1528-1544
    Type
    a
  6. Thelwall, M.; Kousha, K.: Online presentations as a source of scientific impact? : an analysis of PowerPoint files citing academic journals (2008) 0.00
    0.0035052493 = product of:
      0.008763123 = sum of:
        0.0048162127 = weight(_text_:a in 1614) [ClassicSimilarity], result of:
          0.0048162127 = score(doc=1614,freq=4.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.090081796 = fieldWeight in 1614, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1614)
        0.003946911 = product of:
          0.007893822 = sum of:
            0.007893822 = weight(_text_:information in 1614) [ClassicSimilarity], result of:
              0.007893822 = score(doc=1614,freq=2.0), product of:
                0.08139861 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046368346 = queryNorm
                0.09697737 = fieldWeight in 1614, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=1614)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.5, S.805-815
    Type
    a
  7. Thelwall, M.; Sud, P.; Vis, F.: Commenting on YouTube videos : From guatemalan rock to El Big Bang (2012) 0.00
    0.0035052493 = product of:
      0.008763123 = sum of:
        0.0048162127 = weight(_text_:a in 63) [ClassicSimilarity], result of:
          0.0048162127 = score(doc=63,freq=4.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.090081796 = fieldWeight in 63, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=63)
        0.003946911 = product of:
          0.007893822 = sum of:
            0.007893822 = weight(_text_:information in 63) [ClassicSimilarity], result of:
              0.007893822 = score(doc=63,freq=2.0), product of:
                0.08139861 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046368346 = queryNorm
                0.09697737 = fieldWeight in 63, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=63)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    YouTube is one of the world's most popular websites and hosts numerous amateur and professional videos. Comments on these videos might be researched to give insights into audience reactions to important issues or particular videos. Yet, little is known about YouTube discussions in general: how frequent they are, who typically participates, and the role of sentiment. This article fills this gap through an analysis of large samples of text comments on YouTube videos. The results identify patterns and give some benchmarks against which future YouTube research into individual videos can be compared. For instance, the typical YouTube comment was mildly positive, was posted by a 29-year-old male, and contained 58 characters. About 23% of comments in the complete comment sets were replies to previous comments. There was no typical density of discussion on YouTube videos in the sense of the proportion of replies to other comments: videos with both few and many replies were common. The YouTube audience engaged with each other disproportionately when making negative comments, however; positive comments elicited few replies. The biggest trigger of discussion seemed to be religion, whereas the videos attracting the least discussion were predominantly from the Music, Comedy, and How to & Style categories. This suggests different audience uses for YouTube, from passive entertainment to active debating.
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.3, S.616-629
    Type
    a
  8. Thelwall, M.: Female citation impact superiority 1996-2018 in six out of seven English-speaking nations (2020) 0.00
    0.0035052493 = product of:
      0.008763123 = sum of:
        0.0048162127 = weight(_text_:a in 5948) [ClassicSimilarity], result of:
          0.0048162127 = score(doc=5948,freq=4.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.090081796 = fieldWeight in 5948, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5948)
        0.003946911 = product of:
          0.007893822 = sum of:
            0.007893822 = weight(_text_:information in 5948) [ClassicSimilarity], result of:
              0.007893822 = score(doc=5948,freq=2.0), product of:
                0.08139861 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046368346 = queryNorm
                0.09697737 = fieldWeight in 5948, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5948)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    Efforts to combat continuing gender inequalities in academia need to be informed by evidence about where differences occur. Citations are relevant as potential evidence in appointment and promotion decisions, but it is unclear whether there have been historical gender differences in average citation impact that might explain the current shortfall of senior female academics. This study investigates the evolution of gender differences in citation impact 1996-2018 for six million articles from seven large English-speaking nations: Australia, Canada, Ireland, Jamaica, New Zealand, UK, and the USA. The results show that a small female citation advantage has been the norm over time for all these countries except the USA, where there has been no practical difference. The female citation advantage is largest, and statistically significant in most years, for Australia and the UK. This suggests that any academic bias against citing female-authored research cannot explain current employment inequalities. Nevertheless, comparisons using recent citation data, or avoiding it altogether, during appointments or promotion may disadvantage females in some countries by underestimating the likely greater impact of their work, especially in the long term.
    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.8, S.979-990
    Type
    a
  9. Thelwall, M.; Kousha, K.; Stuart, E.; Makita, M.; Abdoli, M.; Wilson, P.; Levitt, J.: In which fields are citations indicators of research quality? (2023) 0.00
    0.0035052493 = product of:
      0.008763123 = sum of:
        0.0048162127 = weight(_text_:a in 1033) [ClassicSimilarity], result of:
          0.0048162127 = score(doc=1033,freq=4.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.090081796 = fieldWeight in 1033, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1033)
        0.003946911 = product of:
          0.007893822 = sum of:
            0.007893822 = weight(_text_:information in 1033) [ClassicSimilarity], result of:
              0.007893822 = score(doc=1033,freq=2.0), product of:
                0.08139861 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046368346 = queryNorm
                0.09697737 = fieldWeight in 1033, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=1033)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Abstract
    Citation counts are widely used as indicators of research quality to support or replace human peer review and for lists of top cited papers, researchers, and institutions. Nevertheless, the relationship between citations and research quality is poorly evidenced. We report the first large-scale science-wide academic evaluation of the relationship between research quality and citations (field normalized citation counts), correlating them for 87,739 journal articles in 34 field-based UK Units of Assessment (UoA). The two correlate positively in all academic fields, from very weak (0.1) to strong (0.5), reflecting broadly linear relationships in all fields. We give the first evidence that the correlations are positive even across the arts and humanities. The patterns are similar for the field classification schemes of Scopus and Dimensions.ai, although varying for some individual subjects and therefore more uncertain for these. We also show for the first time that no field has a citation threshold beyond which all articles are excellent quality, so lists of top cited articles are not pure collections of excellence, and neither is any top citation percentile indicator. Thus, while appropriately field normalized citations associate positively with research quality in all fields, they never perfectly reflect it, even at high values.
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.8, S.941-953
    Type
    a
  10. Kousha, K.; Thelwall, M.; Rezaie, S.: Assessing the citation impact of books : the role of Google Books, Google Scholar, and Scopus (2011) 0.00
    0.002940995 = product of:
      0.007352487 = sum of:
        0.0034055763 = weight(_text_:a in 4920) [ClassicSimilarity], result of:
          0.0034055763 = score(doc=4920,freq=2.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.06369744 = fieldWeight in 4920, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4920)
        0.003946911 = product of:
          0.007893822 = sum of:
            0.007893822 = weight(_text_:information in 4920) [ClassicSimilarity], result of:
              0.007893822 = score(doc=4920,freq=2.0), product of:
                0.08139861 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046368346 = queryNorm
                0.09697737 = fieldWeight in 4920, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4920)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.11, S.2147-2164
    Type
    a
  11. Wilkinson, D.; Thelwall, M.: Trending Twitter topics in English : an international comparison (2012) 0.00
    0.002940995 = product of:
      0.007352487 = sum of:
        0.0034055763 = weight(_text_:a in 375) [ClassicSimilarity], result of:
          0.0034055763 = score(doc=375,freq=2.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.06369744 = fieldWeight in 375, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=375)
        0.003946911 = product of:
          0.007893822 = sum of:
            0.007893822 = weight(_text_:information in 375) [ClassicSimilarity], result of:
              0.007893822 = score(doc=375,freq=2.0), product of:
                0.08139861 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046368346 = queryNorm
                0.09697737 = fieldWeight in 375, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=375)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.8, S.1631-1646
    Type
    a
  12. Thelwall, M.; Maflahi, N.: Academic collaboration rates and citation associations vary substantially between countries and fields (2020) 0.00
    0.002940995 = product of:
      0.007352487 = sum of:
        0.0034055763 = weight(_text_:a in 5952) [ClassicSimilarity], result of:
          0.0034055763 = score(doc=5952,freq=2.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.06369744 = fieldWeight in 5952, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5952)
        0.003946911 = product of:
          0.007893822 = sum of:
            0.007893822 = weight(_text_:information in 5952) [ClassicSimilarity], result of:
              0.007893822 = score(doc=5952,freq=2.0), product of:
                0.08139861 = queryWeight, product of:
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.046368346 = queryNorm
                0.09697737 = fieldWeight in 5952, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  1.7554779 = idf(docFreq=20772, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5952)
          0.5 = coord(1/2)
      0.4 = coord(2/5)
    
    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.8, S.968-978
    Type
    a
  13. Thelwall, M.: Web impact factors and search engine coverage (2000) 0.00
    0.0018875621 = product of:
      0.009437811 = sum of:
        0.009437811 = weight(_text_:a in 4539) [ClassicSimilarity], result of:
          0.009437811 = score(doc=4539,freq=6.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.17652355 = fieldWeight in 4539, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=4539)
      0.2 = coord(1/5)
    
    Abstract
    Search engines index only a proportion of the web and this proportion is not determined randomly but by following algorithms that take into account the properties that impact factors measure. A survey was conducted in order to test the coverage of search engines and to decide thether their partial coverage is indeed an obstacle to using them to calculate web impact factors. The results indicate that search engine coverage, even of large national domains is extremely uneven and would be likely to lead to misleading calculations
    Type
    a
  14. Thelwall, M.: ¬A comparison of link and URL citation counting (2011) 0.00
    0.0018020617 = product of:
      0.009010308 = sum of:
        0.009010308 = weight(_text_:a in 4533) [ClassicSimilarity], result of:
          0.009010308 = score(doc=4533,freq=14.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.1685276 = fieldWeight in 4533, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4533)
      0.2 = coord(1/5)
    
    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.
    Type
    a
  15. Thelwall, M.; Bourrier, M.K.: ¬The reading background of Goodreads book club members : a female fiction canon? (2019) 0.00
    0.0018020617 = product of:
      0.009010308 = sum of:
        0.009010308 = weight(_text_:a in 5461) [ClassicSimilarity], result of:
          0.009010308 = score(doc=5461,freq=14.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.1685276 = fieldWeight in 5461, product of:
              3.7416575 = tf(freq=14.0), with freq of:
                14.0 = termFreq=14.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5461)
      0.2 = coord(1/5)
    
    Abstract
    Purpose Despite the social, educational and therapeutic benefits of book clubs, little is known about which books participants are likely to have read. In response, the purpose of this paper is to investigate the public bookshelves of those that have joined a group within the Goodreads social network site. Design/methodology/approach Books listed as read by members of 50 large English-language Goodreads groups - with a genre focus or other theme - were compiled by author and title. Findings Recent and youth-oriented fiction dominate the 50 books most read by book club members, whilst almost half are works of literature frequently taught at the secondary and postsecondary level (literary classics). Whilst J.K. Rowling is almost ubiquitous (at least 63 per cent as frequently listed as other authors in any group, including groups for other genres), most authors, including Shakespeare (15 per cent), Goulding (6 per cent) and Hemmingway (9 per cent), are little read by some groups. Nor are individual recent literary prize winners or works in languages other than English frequently read. Research limitations/implications Although these results are derived from a single popular website, knowing more about what book club members are likely to have read should help participants, organisers and moderators. For example, recent literary prize winners might be a good choice, given that few members may have read them. Originality/value This is the first large scale study of book group members' reading patterns. Whilst typical reading is likely to vary by group theme and average age, there seems to be a mainly female canon of about 14 authors and 19 books that Goodreads book club members are likely to have read.
    Type
    a
  16. Thelwall, M.: Results from a web impact factor crawler (2001) 0.00
    0.0015230201 = product of:
      0.0076151006 = sum of:
        0.0076151006 = weight(_text_:a in 4490) [ClassicSimilarity], result of:
          0.0076151006 = score(doc=4490,freq=10.0), product of:
            0.053464882 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046368346 = queryNorm
            0.14243183 = fieldWeight in 4490, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4490)
      0.2 = coord(1/5)
    
    Abstract
    Web impact factors, the proposed web equivalent of impact factors for journals, can be calculated by using search engines. It has been found that the results are problematic because of the variable coverage of search engines as well as their ability to give significantly different results over short periods of time. The fundamental problem is that although some search engines provide a functionality that is capable of being used for impact calculations, this is not their primary task and therefore they do not give guarantees as to performance in this respect. In this paper, a bespoke web crawler designed specifically for the calculation of reliable WIFs is presented. This crawler was used to calculate WIFs for a number of UK universities, and the results of these calculations are discussed. The principal findings were that with certain restrictions, WIFs can be calculated reliably, but do not correlate with accepted research rankings owing to the variety of material hosted on university servers. Changes to the calculations to improve the fit of the results to research rankings are proposed, but there are still inherent problems undermining the reliability of the calculation. These problems still apply if the WIF scores are taken on their own as indicators of the general impact of any area of the Internet, but with care would not apply to online journals.
    Type
    a