Search (117 results, page 1 of 6)

0.046900414 = product of:
  0.11725103 = sum of:
    0.09940575 = weight(_text_:philosophy in 1234) [ClassicSimilarity], result of:
      0.09940575 = score(doc=1234,freq=4.0), product of:
        0.23055021 = queryWeight, product of:
          5.5189433 = idf(docFreq=481, maxDocs=44218)
          0.04177434 = queryNorm
        0.43116745 = fieldWeight in 1234, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.5189433 = idf(docFreq=481, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1234)
    0.017845279 = weight(_text_:of in 1234) [ClassicSimilarity], result of:
      0.017845279 = score(doc=1234,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.27317715 = fieldWeight in 1234, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1234)
  0.4 = coord(2/5)

Abstract

Academic social network sites Academia.edu and ResearchGate, and reference sharing sites Mendeley, Bibsonomy, Zotero, and CiteULike, give scholars the ability to publicize their research outputs and connect with each other. With millions of users, these are a significant addition to the scholarly communication and academic information-seeking eco-structure. There is thus a need to understand the role that they play and the changes, if any, that they can make to the dynamics of academic careers. This article investigates attributes of philosophy scholars on Academia.edu, introducing a median-based, time-normalizing method to adjust for time delays in joining the site. In comparison to students, faculty tend to attract more profile views but female philosophers did not attract more profile views than did males, suggesting that academic capital drives philosophy uses of the site more than does friendship and networking. Secondary analyses of law, history, and computer science confirmed the faculty advantage (in terms of higher profile views) except for females in law and females in computer science. There was also a female advantage for both faculty and students in law and computer science as well as for history students. Hence, Academia.edu overall seems to reflect a hybrid of scholarly norms (the faculty advantage) and a female advantage that is suggestive of general social networking norms. Finally, traditional bibliometric measures did not correlate with any Academia.edu metrics for philosophers, perhaps because more senior academics use the site less extensively or because of the range informal scholarly activities that cannot be measured by bibliometric methods.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.4, S.721-731

0.045734476 = product of:
  0.114336185 = sum of:
    0.09940575 = weight(_text_:philosophy in 1331) [ClassicSimilarity], result of:
      0.09940575 = score(doc=1331,freq=4.0), product of:
        0.23055021 = queryWeight, product of:
          5.5189433 = idf(docFreq=481, maxDocs=44218)
          0.04177434 = queryNorm
        0.43116745 = fieldWeight in 1331, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.5189433 = idf(docFreq=481, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1331)
    0.014930432 = weight(_text_:of in 1331) [ClassicSimilarity], result of:
      0.014930432 = score(doc=1331,freq=14.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.22855641 = fieldWeight in 1331, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1331)
  0.4 = coord(2/5)

Abstract

Some curricula vitae (web CVs) of academics on the web, including homepages and publication lists, link to open-access (OA) articles, resources, abstracts in publishers' websites, or academic discussions, helping to disseminate research. To assess how common such practices are and whether they vary by discipline, gender, and country, the authors conducted a large-scale e-mail survey of astronomy and astrophysics, public health, environmental engineering, and philosophy across 15 European countries and analyzed hyperlinks from web CVs of academics. About 60% of the 2,154 survey responses reported having a web CV or something similar, and there were differences between disciplines, genders, and countries. A follow-up outlink analysis of 2,700 web CVs found that a third had at least one outlink to an OA target, typically a public eprint archive or an individual self-archived file. This proportion was considerably higher in astronomy (48%) and philosophy (37%) than in environmental engineering (29%) and public health (21%). There were also differences in linking to publishers' websites, resources, and discussions. Perhaps most important, however, the amount of linking to OA publications seems to be much lower than allowed by publishers and journals, suggesting that many opportunities for disseminating full-text research online are being missed, especially in disciplines without established repositories. Moreover, few academics seem to be exploiting their CVs to link to discussions, resources, or article abstracts, which seems to be another missed opportunity for publicizing research.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.8, S.1615-1626

0.034088366 = product of:
  0.08522091 = sum of:
    0.070290476 = weight(_text_:philosophy in 4920) [ClassicSimilarity], result of:
      0.070290476 = score(doc=4920,freq=2.0), product of:
        0.23055021 = queryWeight, product of:
          5.5189433 = idf(docFreq=481, maxDocs=44218)
          0.04177434 = queryNorm
        0.30488142 = fieldWeight in 4920, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.5189433 = idf(docFreq=481, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4920)
    0.014930432 = weight(_text_:of in 4920) [ClassicSimilarity], result of:
      0.014930432 = score(doc=4920,freq=14.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.22855641 = fieldWeight in 4920, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4920)
  0.4 = coord(2/5)

Abstract

Citation indictors are increasingly used in some subject areas to support peer review in the evaluation of researchers and departments. Nevertheless, traditional journal-based citation indexes may be inadequate for the citation impact assessment of book-based disciplines. This article examines whether online citations from Google Books and Google Scholar can provide alternative sources of citation evidence. To investigate this, we compared the citation counts to 1,000 books submitted to the 2008 U.K. Research Assessment Exercise (RAE) from Google Books and Google Scholar with Scopus citations across seven book-based disciplines (archaeology; law; politics and international studies; philosophy; sociology; history; and communication, cultural, and media studies). Google Books and Google Scholar citations to books were 1.4 and 3.2 times more common than were Scopus citations, and their medians were more than twice and three times as high as were Scopus median citations, respectively. This large number of citations is evidence that in book-oriented disciplines in the social sciences, arts, and humanities, online book citations may be sufficiently numerous to support peer review for research evaluation, at least in the United Kingdom.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.11, S.2147-2164

0.015357548 = product of:
  0.03839387 = sum of:
    0.021414334 = weight(_text_:of in 4345) [ClassicSimilarity], result of:
      0.021414334 = score(doc=4345,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.32781258 = fieldWeight in 4345, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=4345)
    0.016979538 = product of:
      0.033959076 = sum of:
        0.033959076 = weight(_text_:22 in 4345) [ClassicSimilarity], result of:
          0.033959076 = score(doc=4345,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.23214069 = fieldWeight in 4345, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=4345)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

The microblogging site Twitter generates a constant stream of communication, some of which concerns events of general interest. An analysis of Twitter may, therefore, give insights into why particular events resonate with the population. This article reports a study of a month of English Twitter posts, assessing whether popular events are typically associated with increases in sentiment strength, as seems intuitively likely. Using the top 30 events, determined by a measure of relative increase in (general) term usage, the results give strong evidence that popular events are normally associated with increases in negative sentiment strength and some evidence that peaks of interest in events have stronger positive sentiment than the time before the peak. It seems that many positive events, such as the Oscars, are capable of generating increased negative sentiment in reaction to them. Nevertheless, the surprisingly small average change in sentiment associated with popular events (typically 1% and only 6% for Tiger Woods' confessions) is consistent with events affording posters opportunities to satisfy pre-existing personal goals more often than eliciting instinctive reactions.

Date

22. 1.2011 14:27:06

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.406-418

0.015357548 = product of:
  0.03839387 = sum of:
    0.021414334 = weight(_text_:of in 2856) [ClassicSimilarity], result of:
      0.021414334 = score(doc=2856,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.32781258 = fieldWeight in 2856, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2856)
    0.016979538 = product of:
      0.033959076 = sum of:
        0.033959076 = weight(_text_:22 in 2856) [ClassicSimilarity], result of:
          0.033959076 = score(doc=2856,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.23214069 = fieldWeight in 2856, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2856)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

This article introduces a new source of evidence of the value of medical-related research: citations from clinical guidelines. These give evidence that research findings have been used to inform the day-to-day practice of medical staff. To identify whether citations from guidelines can give different information from that of traditional citation counts, this article assesses the extent to which references in clinical guidelines tend to be highly cited in the academic literature and highly read in Mendeley. Using evidence from the United Kingdom, references associated with the UK's National Institute of Health and Clinical Excellence (NICE) guidelines tended to be substantially more cited than comparable articles, unless they had been published in the most recent 3 years. Citation counts also seemed to be stronger indicators than Mendeley readership altmetrics. Hence, although presence in guidelines may be particularly useful to highlight the contributions of recently published articles, for older articles citation counts may already be sufficient to recognize their contributions to health in society.

Date

19. 3.2016 12:22:00

Source

Journal of the Association for Information Science and Technology. 67(2016) no.4, S.960-966

0.015357548 = product of:
  0.03839387 = sum of:
    0.021414334 = weight(_text_:of in 4291) [ClassicSimilarity], result of:
      0.021414334 = score(doc=4291,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.32781258 = fieldWeight in 4291, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=4291)
    0.016979538 = product of:
      0.033959076 = sum of:
        0.033959076 = weight(_text_:22 in 4291) [ClassicSimilarity], result of:
          0.033959076 = score(doc=4291,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.23214069 = fieldWeight in 4291, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=4291)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Counts of tweets and Mendeley user libraries have been proposed as altmetric alternatives to citation counts for the impact assessment of articles. Although both have been investigated to discover whether they correlate with article citations, it is not known whether users tend to tweet or save (in Mendeley) the same kinds of articles that they cite. In response, this article compares pairs of articles that are tweeted, saved to a Mendeley library, or cited by the same user, but possibly a different user for each source. The study analyzes 1,131,318 articles published in 2012, with minimum tweeted (10), saved to Mendeley (100), and cited (10) thresholds. The results show surprisingly minor overall overlaps between the three phenomena. The importance of journals for Twitter and the presence of many bots at different levels of activity suggest that this site has little value for impact altmetrics. The moderate differences between patterns of saving and citation suggest that Mendeley can be used for some types of impact assessments, but sensitivity is needed for underlying differences.

Date

28. 7.2018 10:00:22

Source

Journal of the Association for Information Science and Technology. 69(2018) no.8, S.959-973

0.014388752 = product of:
  0.03597188 = sum of:
    0.015961302 = weight(_text_:of in 2734) [ClassicSimilarity], result of:
      0.015961302 = score(doc=2734,freq=16.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.24433708 = fieldWeight in 2734, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2734)
    0.02001058 = product of:
      0.04002116 = sum of:
        0.04002116 = weight(_text_:22 in 2734) [ClassicSimilarity], result of:
          0.04002116 = score(doc=2734,freq=4.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.27358043 = fieldWeight in 2734, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2734)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Collaboration is a major research policy objective, but does it deliver higher quality research? This study uses citation analysis to examine the Web of Science (WoS) Information Science & Library Science subject category (IS&LS) to ascertain whether, in general, more highly cited articles are more highly collaborative than other articles. It consists of two investigations. The first investigation is a longitudinal comparison of the degree and proportion of collaboration in five strata of citation; it found that collaboration in the highest four citation strata (all in the most highly cited 22%) increased in unison over time, whereas collaboration in the lowest citation strata (un-cited articles) remained low and stable. Given that over 40% of the articles were un-cited, it seems important to take into account the differences found between un-cited articles and relatively highly cited articles when investigating collaboration in IS&LS. The second investigation compares collaboration for 35 influential information scientists; it found that their more highly cited articles on average were not more highly collaborative than their less highly cited articles. In summary, although collaborative research is conducive to high citation in general, collaboration has apparently not tended to be essential to the success of current and former elite information scientists.

Date

22. 3.2009 12:43:51

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.3, S.434-442

0.013798547 = product of:
  0.034496367 = sum of:
    0.02034675 = weight(_text_:of in 586) [ClassicSimilarity], result of:
      0.02034675 = score(doc=586,freq=26.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.31146988 = fieldWeight in 586, product of:
          5.0990195 = tf(freq=26.0), with freq of:
            26.0 = termFreq=26.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=586)
    0.0141496165 = product of:
      0.028299233 = sum of:
        0.028299233 = weight(_text_:22 in 586) [ClassicSimilarity], result of:
          0.028299233 = score(doc=586,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.19345059 = fieldWeight in 586, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=586)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Although the analysis of citations in the scholarly literature is now an established and relatively well understood part of information science, not enough is known about citations that can be found on the Web. In particular, are there new Web types, and if so, are these trivial or potentially useful for studying or evaluating research communication? We sought evidence based upon a sample of 1,577 Web citations of the URLs or titles of research articles in 64 open-access journals from biology, physics, chemistry, and computing. Only 25% represented intellectual impact, from references of Web documents (23%) and other informal scholarly sources (2%). Many of the Web/URL citations were created for general or subject-specific navigation (45%) or for self-publicity (22%). Additional analyses revealed significant disciplinary differences in the types of Google unique Web/URL citations as well as some characteristics of scientific open-access publishing on the Web. We conclude that the Web provides access to a new and different type of citation information, one that may therefore enable us to measure different aspects of research, and the research process in particular; but to obtain good information, the different types should be separated.

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.11, S.1631-1644

0.012797958 = product of:
  0.031994894 = sum of:
    0.017845279 = weight(_text_:of in 57) [ClassicSimilarity], result of:
      0.017845279 = score(doc=57,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.27317715 = fieldWeight in 57, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=57)
    0.0141496165 = product of:
      0.028299233 = sum of:
        0.028299233 = weight(_text_:22 in 57) [ClassicSimilarity], result of:
          0.028299233 = score(doc=57,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.19345059 = fieldWeight in 57, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=57)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Webometric network analyses have been used to map the connectivity of groups of websites to identify clusters, important sites or overall structure. Such analyses have mainly been based upon hyperlink counts, the number of hyperlinks between a pair of websites, although some have used title mentions or URL citations instead. The ability to automatically gather hyperlink counts from Yahoo! ceased in April 2011 and the ability to manually gather such counts was due to cease by early 2012, creating a need for alternatives. This article assesses URL citations and title mentions as possible replacements for hyperlinks in both binary and weighted direct link and co-inlink network diagrams. It also assesses three different types of data for the network connections: hit count estimates, counts of matching URLs, and filtered counts of matching URLs. Results from analyses of U.S. library and information science departments and U.K. universities give evidence that metrics based upon URLs or titles can be appropriate replacements for metrics based upon hyperlinks for both binary and weighted networks, although filtered counts of matching URLs are necessary to give the best results for co-title mention and co-URL citation network diagrams.

Date

6. 4.2012 18:16:22

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.4, S.805-816

0.012797958 = product of:
  0.031994894 = sum of:
    0.017845279 = weight(_text_:of in 2593) [ClassicSimilarity], result of:
      0.017845279 = score(doc=2593,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.27317715 = fieldWeight in 2593, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2593)
    0.0141496165 = product of:
      0.028299233 = sum of:
        0.028299233 = weight(_text_:22 in 2593) [ClassicSimilarity], result of:
          0.028299233 = score(doc=2593,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.19345059 = fieldWeight in 2593, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2593)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Purpose The four major Subject Repositories (SRs), arXiv, Research Papers in Economics (RePEc), Social Science Research Network (SSRN) and PubMed Central (PMC), are all important within their disciplines but no previous study has systematically compared how often they are cited in academic publications. In response, the purpose of this paper is to report an analysis of citations to SRs from Scopus publications, 2000-2013. Design/methodology/approach Scopus searches were used to count the number of documents citing the four SRs in each year. A random sample of 384 documents citing the four SRs was then visited to investigate the nature of the citations. Findings Each SR was most cited within its own subject area but attracted substantial citations from other subject areas, suggesting that they are open to interdisciplinary uses. The proportion of documents citing each SR is continuing to increase rapidly, and the SRs all seem to attract substantial numbers of citations from more than one discipline. Research limitations/implications Scopus does not cover all publications, and most citations to documents found in the four SRs presumably cite the published version, when one exists, rather than the repository version. Practical implications SRs are continuing to grow and do not seem to be threatened by institutional repositories and so research managers should encourage their continued use within their core disciplines, including for research that aims at an audience in other disciplines. Originality/value This is the first simultaneous analysis of Scopus citations to the four most popular SRs.

Date

20. 1.2015 18:30:22

Source

Aslib journal of information management. 67(2015) no.6, S.614-635

0.012797958 = product of:
  0.031994894 = sum of:
    0.017845279 = weight(_text_:of in 3806) [ClassicSimilarity], result of:
      0.017845279 = score(doc=3806,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.27317715 = fieldWeight in 3806, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3806)
    0.0141496165 = product of:
      0.028299233 = sum of:
        0.028299233 = weight(_text_:22 in 3806) [ClassicSimilarity], result of:
          0.028299233 = score(doc=3806,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.19345059 = fieldWeight in 3806, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3806)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Purpose Mendeley reader counts have been proposed as early indicators for the impact of academic publications. The purpose of this paper is to assess whether there are enough Mendeley readers for research evaluation purposes during the month when an article is first published. Design/methodology/approach Average Mendeley reader counts were compared to the average Scopus citation counts for 104,520 articles from ten disciplines during the second half of 2016. Findings Articles attracted, on average, between 0.1 and 0.8 Mendeley readers per article in the month in which they first appeared in Scopus. This is about ten times more than the average Scopus citation count. Research limitations/implications Other disciplines may use Mendeley more or less than the ten investigated here. The results are dependent on Scopus's indexing practices, and Mendeley reader counts can be manipulated and have national and seniority biases. Practical implications Mendeley reader counts during the month of publication are more powerful than Scopus citations for comparing the average impacts of groups of documents but are not high enough to differentiate between the impacts of typical individual articles. Originality/value This is the first multi-disciplinary and systematic analysis of Mendeley reader counts from the publication month of an article.

Date

20. 1.2015 18:30:22

Source

Aslib journal of information management. 69(2017) no.2, S.174-183

0.012431653 = product of:
  0.031079132 = sum of:
    0.016929517 = weight(_text_:of in 4200) [ClassicSimilarity], result of:
      0.016929517 = score(doc=4200,freq=18.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.25915858 = fieldWeight in 4200, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4200)
    0.0141496165 = product of:
      0.028299233 = sum of:
        0.028299233 = weight(_text_:22 in 4200) [ClassicSimilarity], result of:
          0.028299233 = score(doc=4200,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.19345059 = fieldWeight in 4200, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4200)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

A huge number of informal messages are posted every day in social network sites, blogs, and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online arguments. Algorithms to identify sentiment and sentiment strength are needed to help understand the role of emotion in this informal communication and also to identify inappropriate or anomalous affective utterances, potentially associated with threatening behavior to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially oriented, designed to identify opinions about products rather than user behaviors. This article partly fills this gap with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de facto grammars and spelling styles of cyberspace. Applied to MySpace comments and with a lookup table of term sentiment strengths optimized by machine learning, SentiStrength is able to predict positive emotion with 60.6% accuracy and negative emotion with 72.8% accuracy, both based upon strength scales of 1-5. The former, but not the latter, is better than baseline and a wide range of general machine learning approaches.

Date

22. 1.2011 14:29:23

Footnote

Vgl. auch das Erratum in: Journal of the American Society for Information Science and Technology. 62(2011) no.2, S.419

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.12, S.2544-2558

0.012209262 = product of:
  0.030523153 = sum of:
    0.013543615 = weight(_text_:of in 3211) [ClassicSimilarity], result of:
      0.013543615 = score(doc=3211,freq=8.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.20732689 = fieldWeight in 3211, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3211)
    0.016979538 = product of:
      0.033959076 = sum of:
        0.033959076 = weight(_text_:22 in 3211) [ClassicSimilarity], result of:
          0.033959076 = score(doc=3211,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.23214069 = fieldWeight in 3211, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=3211)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Scientists and managers using citation-based indicators to help evaluate research cannot evaluate recent articles because of the time needed for citations to accrue. Reading occurs before citing, however, and so it makes sense to count readers rather than citations for recent publications. To assess this, Mendeley readers and citations were obtained for articles from 2004 to late 2014 in five broad categories (agriculture, business, decision science, pharmacy, and the social sciences) and 50 subcategories. In these areas, citation counts tended to increase with every extra year since publication, and readership counts tended to increase faster initially but then stabilize after about 5 years. The correlation between citations and readers was also higher for longer time periods, stabilizing after about 5 years. Although there were substantial differences between broad fields and smaller differences between subfields, the results confirm the value of Mendeley reader counts as early scientific impact indicators.

Date

16.11.2016 11:07:22

Source

Journal of the Association for Information Science and Technology. 67(2016) no.12, S.3036-3050

0.01211739 = product of:
  0.030293474 = sum of:
    0.009978054 = product of:
      0.04989027 = sum of:
        0.04989027 = weight(_text_:problem in 3764) [ClassicSimilarity], result of:
          0.04989027 = score(doc=3764,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.28137225 = fieldWeight in 3764, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.046875 = fieldNorm(doc=3764)
      0.2 = coord(1/5)
    0.02031542 = weight(_text_:of in 3764) [ClassicSimilarity], result of:
      0.02031542 = score(doc=3764,freq=18.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.3109903 = fieldWeight in 3764, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3764)
  0.4 = coord(2/5)

Abstract

Patents sometimes cite webpages either as general background to the problem being addressed or to identify prior publications that limit the scope of the patent granted. Counts of the number of patents citing an organization's website may therefore provide an indicator of its technological capacity or relevance. This article introduces methods to extract URL citations from patents and evaluates the usefulness of counts of patent web citations as a technology indicator. An analysis of patents citing 200 US universities or 177 UK universities found computer science and engineering departments to be frequently cited, as well as research-related webpages, such as Wikipedia, YouTube, or the Internet Archive. Overall, however, patent URL citations seem to be frequent enough to be useful for ranking major US and the top few UK universities if popular hosted subdomains are filtered out, but the hit count estimates on the first search engine results page should not be relied upon for accuracy.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.8, S.1967-1974

0.012068383 = product of:
  0.030170958 = sum of:
    0.008315044 = product of:
      0.041575223 = sum of:
        0.041575223 = weight(_text_:problem in 4490) [ClassicSimilarity], result of:
          0.041575223 = score(doc=4490,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.23447686 = fieldWeight in 4490, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4490)
      0.2 = coord(1/5)
    0.021855915 = weight(_text_:of in 4490) [ClassicSimilarity], result of:
      0.021855915 = score(doc=4490,freq=30.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.33457235 = fieldWeight in 4490, product of:
          5.477226 = tf(freq=30.0), with freq of:
            30.0 = termFreq=30.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4490)
  0.4 = coord(2/5)

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.

Source

Journal of documentation. 57(2001) no.2, S.177-191

0.0107072545 = product of:
  0.026768135 = sum of:
    0.012618518 = weight(_text_:of in 178) [ClassicSimilarity], result of:
      0.012618518 = score(doc=178,freq=10.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.19316542 = fieldWeight in 178, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=178)
    0.0141496165 = product of:
      0.028299233 = sum of:
        0.028299233 = weight(_text_:22 in 178) [ClassicSimilarity], result of:
          0.028299233 = score(doc=178,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.19345059 = fieldWeight in 178, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=178)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Purpose Public attitudes towards COVID-19 and social distancing are critical in reducing its spread. It is therefore important to understand public reactions and information dissemination in all major forms, including on social media. This article investigates important issues reflected on Twitter in the early stages of the public reaction to COVID-19. Design/methodology/approach A thematic analysis of the most retweeted English-language tweets mentioning COVID-19 during March 10-29, 2020. Findings The main themes identified for the 87 qualifying tweets accounting for 14 million retweets were: lockdown life; attitude towards social restrictions; politics; safety messages; people with COVID-19; support for key workers; work; and COVID-19 facts/news. Research limitations/implications Twitter played many positive roles, mainly through unofficial tweets. Users shared social distancing information, helped build support for social distancing, criticised government responses, expressed support for key workers and helped each other cope with social isolation. A few popular tweets not supporting social distancing show that government messages sometimes failed. Practical implications Public health campaigns in future may consider encouraging grass roots social web activity to support campaign goals. At a methodological level, analysing retweet counts emphasised politics and ignored practical implementation issues. Originality/value This is the first qualitative analysis of general COVID-19-related retweeting.

Date

20. 1.2015 18:30:22

Source

Aslib journal of information management. 72(2020) no.6, S.945-962

0.0107072545 = product of:
  0.026768135 = sum of:
    0.012618518 = weight(_text_:of in 995) [ClassicSimilarity], result of:
      0.012618518 = score(doc=995,freq=10.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.19316542 = fieldWeight in 995, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=995)
    0.0141496165 = product of:
      0.028299233 = sum of:
        0.028299233 = weight(_text_:22 in 995) [ClassicSimilarity], result of:
          0.028299233 = score(doc=995,freq=2.0), product of:
            0.14628662 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04177434 = queryNorm
            0.19345059 = fieldWeight in 995, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=995)
      0.5 = coord(1/2)
  0.4 = coord(2/5)

Abstract

Collaboration is encouraged because it is believed to improve academic research, supported by indirect evidence in the form of more coauthored articles being more cited. Nevertheless, this might not reflect quality but increased self-citations or the "audience effect": citations from increased awareness through multiple author networks. We address this with the first science wide investigation into whether author numbers associate with journal article quality, using expert peer quality judgments for 122,331 articles from the 2014-20 UK national assessment. Spearman correlations between author numbers and quality scores show moderately strong positive associations (0.2-0.4) in the health, life, and physical sciences, but weak or no positive associations in engineering and social sciences, with weak negative/positive or no associations in various arts and humanities, and a possible negative association for decision sciences. This gives the first systematic evidence that greater numbers of authors associates with higher quality journal articles in the majority of academia outside the arts and humanities, at least for the UK. Positive associations between team size and citation counts in areas with little association between team size and quality also show that audience effects or other nonquality factors account for the higher citation rates of coauthored articles in some fields.

Date

22. 6.2023 18:11:50

Source

Journal of the Association for Information Science and Technology. 74(2023) no.7, S.791-810

0.010097824 = product of:
  0.02524456 = sum of:
    0.008315044 = product of:
      0.041575223 = sum of:
        0.041575223 = weight(_text_:problem in 4279) [ClassicSimilarity], result of:
          0.041575223 = score(doc=4279,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.23447686 = fieldWeight in 4279, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4279)
      0.2 = coord(1/5)
    0.016929517 = weight(_text_:of in 4279) [ClassicSimilarity], result of:
      0.016929517 = score(doc=4279,freq=18.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.25915858 = fieldWeight in 4279, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4279)
  0.4 = coord(2/5)

Abstract

Webometrics, the quantitative study of Web-related phenomena, emerged from the realization that methods originally designed for bibliometric analysis of scientific journal article citation patterns could be applied to the Web, with commercial search engines providing the raw data. Almind and Ingwersen (1997) defined the field and gave it its name. Other pioneers included Rodriguez Gairin (1997) and Aguillo (1998). Larson (1996) undertook exploratory link structure analysis, as did Rousseau (1997). Webometrics encompasses research from fields beyond information science such as communication studies, statistical physics, and computer science. In this review we concentrate on link analysis, but also cover other aspects of webometrics, including Web log fle analysis. One theme that runs through this chapter is the messiness of Web data and the need for data cleansing heuristics. The uncontrolled Web creates numerous problems in the interpretation of results, for instance, from the automatic creation or replication of links. The loose connection between top-level domain specifications (e.g., com, edu, and org) and their actual content is also a frustrating problem. For example, many .com sites contain noncommercial content, although com is ostensibly the main commercial top-level domain. Indeed, a skeptical researcher could claim that obstacles of this kind are so great that all Web analyses lack value. As will be seen, one response to this view, a view shared by critics of evaluative bibliometrics, is to demonstrate that Web data correlate significantly with some non-Web data in order to prove that the Web data are not wholly random. A practical response has been to develop increasingly sophisticated data cleansing techniques and multiple data analysis methods.

Source

Annual review of information science and technology. 39(2005), S.81-138

0.008373425 = product of:
  0.020933563 = sum of:
    0.008315044 = product of:
      0.041575223 = sum of:
        0.041575223 = weight(_text_:problem in 3317) [ClassicSimilarity], result of:
          0.041575223 = score(doc=3317,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.23447686 = fieldWeight in 3317, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3317)
      0.2 = coord(1/5)
    0.012618518 = weight(_text_:of in 3317) [ClassicSimilarity], result of:
      0.012618518 = score(doc=3317,freq=10.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.19316542 = fieldWeight in 3317, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3317)
  0.4 = coord(2/5)

Abstract

Citations from patents to scientific publications provide useful evidence about the commercial impact of academic research, but automatically searchable databases are needed to exploit this connection for large-scale patent citation evaluations. Google covers multiple different international patent office databases but does not index patent citations or allow automatic searches. In response, this article introduces a semiautomatic indirect method via Bing to extract and filter patent citations from Google to academic papers with an overall precision of 98%. The method was evaluated with 322,192 science and engineering Scopus articles from every second year for the period 1996-2012. Although manual Google Patent searches give more results, especially for articles with many patent citations, the difference is not large enough to be a major problem. Within Biomedical Engineering, Biotechnology, and Pharmacology & Pharmaceutics, 7% to 10% of Scopus articles had at least one patent citation but other fields had far fewer, so patent citation analysis is only relevant for a minority of publications. Low but positive correlations between Google Patent citations and Scopus citations across all fields suggest that traditional citation counts cannot substitute for patent citations when evaluating research.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.1, S.48-61

0.0052405605 = product of:
  0.026202802 = sum of:
    0.026202802 = weight(_text_:of in 1676) [ClassicSimilarity], result of:
      0.026202802 = score(doc=1676,freq=22.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.40111488 = fieldWeight in 1676, product of:
          4.690416 = tf(freq=22.0), with freq of:
            22.0 = termFreq=22.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1676)
  0.2 = coord(1/5)

Abstract

Results from recent advances in link metrics have demonstrated that the hyperlink structure of national university systems can be strongly related to the research productivity of the individual institutions. This paper uses a page categorization to show that restricting the metrics to subsets more closely related to the research of the host university can produce even stronger associations. A partial overlap was also found between the effects of applying advanced document models and separating page types, but the best results were achieved through a combination of the two.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.7, S.594-602