Search (62 results, page 1 of 4)

0.071140714 = product of:
  0.28456286 = sum of:
    0.28456286 = weight(_text_:2f in 2188) [ClassicSimilarity], result of:
      0.28456286 = score(doc=2188,freq=2.0), product of:
        0.3797425 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.04479146 = queryNorm
        0.7493574 = fieldWeight in 2188, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.0625 = fieldNorm(doc=2188)
  0.25 = coord(1/4)

Content

Vgl. unter: https://www.leibniz-science20.de%2Fforschung%2Fprojekte%2Faltmetrics-in-verschiedenen-wissenschaftsdisziplinen%2F&ei=2jTgVaaXGcK4Udj1qdgB&usg=AFQjCNFOPdONj4RKBDf9YDJOLuz3lkGYlg&sig2=5YI3KWIGxBmk5_kv0P_8iQ.

0.022583602 = product of:
  0.09033441 = sum of:
    0.09033441 = weight(_text_:engines in 4533) [ClassicSimilarity], result of:
      0.09033441 = score(doc=4533,freq=4.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.39693922 = fieldWeight in 4533, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4533)
  0.25 = coord(1/4)

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.

0.01916282 = product of:
  0.07665128 = sum of:
    0.07665128 = weight(_text_:engines in 1284) [ClassicSimilarity], result of:
      0.07665128 = score(doc=1284,freq=2.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.33681408 = fieldWeight in 1284, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.046875 = fieldNorm(doc=1284)
  0.25 = coord(1/4)

Abstract

This article offers a comparative analysis of the personal profiling capabilities of the two most important free citation-based academic search engines, namely, Microsoft Academic Search (MAS) and Google Scholar Citations (GSC). Author profiles can be useful for evaluation purposes once the advantages and the shortcomings of these services are described and taken into consideration. In total, 771 personal profiles appearing in both the MAS and the GSC databases were analyzed. Results show that the GSC profiles include more documents and citations than those in MAS but with a strong bias toward the information and computing sciences, whereas the MAS profiles are disciplinarily better balanced. MAS shows technical problems such as a higher number of duplicated profiles and a lower updating rate than GSC. It is concluded that both services could be used for evaluation proposes only if they are applied along with other citation indices as a way to supplement that information.

0.015969018 = product of:
  0.06387607 = sum of:
    0.06387607 = weight(_text_:engines in 4626) [ClassicSimilarity], result of:
      0.06387607 = score(doc=4626,freq=2.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.2806784 = fieldWeight in 4626, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4626)
  0.25 = coord(1/4)

Abstract

The primary webometric method for estimating the online impact of an organization is to count links to its website. Link counts have been available from commercial search engines for over a decade but this was set to end by early 2012 and so a replacement is needed. This article compares link counts to two alternative methods: URL citations and organization title mentions. New variations of these methods are also introduced. The three methods are compared against each other using Yahoo!. Two of the three methods (URL citations and organization title mentions) are also compared against each other using Bing. Evidence from a case study of 131 UK universities and 49 US Library and Information Science (LIS) departments suggests that Bing's Hit Count Estimates (HCEs) for popular title searches are not useful for webometric research but that Yahoo!'s HCEs for all three types of search and Bing's URL citation HCEs seem to be consistent. For exact URL counts the results of all three methods in Yahoo! and both methods in Bing are also consistent. Four types of accuracy factors are also introduced and defined: search engine coverage, search engine retrieval variation, search engine retrieval anomalies, and query polysemy.

0.015969018 = product of:
  0.06387607 = sum of:
    0.06387607 = weight(_text_:engines in 4944) [ClassicSimilarity], result of:
      0.06387607 = score(doc=4944,freq=2.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.2806784 = fieldWeight in 4944, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4944)
  0.25 = coord(1/4)

Abstract

This study examines longitudinal trends in the university-industry-government (UIG) relationship on the web in the Korean context by using triple helix (TH) indicators. The study considers various Internet resources, including websites/documents, blogs, online cafes, Knowledge-In (comparable to Yahoo! Answers), and online news sites, by employing webometric and co-word analysis techniques to ascertain longitudinal trends in the UIG relationship, which have received considerable attention in the last decade. The results indicate that the UIG relationship varied according to the government's policies and that there was some tension in the longitudinal UIG relationship. Further, websites/documents and blogs were the most reliable sources for examining the strength of and variations in the bilateral and trilateral UIG relationships on the web. In addition, web-based T(uig) values showed a stronger trilateral relationship and larger variations in the UIG relationship than Science Citation Index-based T(uig) values. The results suggest that various Internet resources (e.g., advanced search engines, websites/documents, blogs, and online cafes), together with TH indicators, can be used to explore the UIG relationship on the web.

0.015969018 = product of:
  0.06387607 = sum of:
    0.06387607 = weight(_text_:engines in 2711) [ClassicSimilarity], result of:
      0.06387607 = score(doc=2711,freq=2.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.2806784 = fieldWeight in 2711, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2711)
  0.25 = coord(1/4)

Abstract

We present PubSearch, a hybrid heuristic scheme for re-ranking academic papers retrieved from standard digital libraries such as the ACM Portal. The scheme is based on the hierarchical combination of a custom implementation of the term frequency heuristic, a time-depreciated citation score and a graph-theoretic computed score that relates the paper's index terms with each other. We designed and developed a meta-search engine that submits user queries to standard digital repositories of academic publications and re-ranks the repository results using the hierarchical heuristic scheme. We evaluate our proposed re-ranking scheme via user feedback against the results of ACM Portal on a total of 58 different user queries specified from 15 different users. The results show that our proposed scheme significantly outperforms ACM Portal in terms of retrieval precision as measured by most common metrics in Information Retrieval including Normalized Discounted Cumulative Gain (NDCG), Expected Reciprocal Rank (ERR) as well as a newly introduced lexicographic rule (LEX) of ranking search results. In particular, PubSearch outperforms ACM Portal by more than 77% in terms of ERR, by more than 11% in terms of NDCG, and by more than 907.5% in terms of LEX. We also re-rank the top-10 results of a subset of the original 58 user queries produced by Google Scholar, Microsoft Academic Search, and ArnetMiner; the results show that PubSearch compares very well against these search engines as well. The proposed scheme can be easily plugged in any existing search engine for retrieval of academic publications.

0.015949111 = product of:
  0.063796446 = sum of:
    0.063796446 = product of:
      0.12759289 = sum of:
        0.12759289 = weight(_text_:programming in 3055) [ClassicSimilarity], result of:
          0.12759289 = score(doc=3055,freq=2.0), product of:
            0.29361802 = queryWeight, product of:
              6.5552235 = idf(docFreq=170, maxDocs=44218)
              0.04479146 = queryNorm
            0.43455404 = fieldWeight in 3055, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.5552235 = idf(docFreq=170, maxDocs=44218)
              0.046875 = fieldNorm(doc=3055)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

Medical research is highly funded and often expensive and so is particularly important to evaluate effectively. Nevertheless, citation counts may accrue too slowly for use in some formal and informal evaluations. It is therefore important to investigate whether alternative metrics could be used as substitutes. This article assesses whether one such altmetric, Mendeley readership counts, correlates strongly with citation counts across all medical fields, whether the relationship is stronger if student readers are excluded, and whether they are distributed similarly to citation counts. Based on a sample of 332,975 articles from 2009 in 45 medical fields in Scopus, citation counts correlated strongly (about 0.7; 78% of articles had at least one reader) with Mendeley readership counts (from the new version 1 applications programming interface [API]) in almost all fields, with one minor exception, and the correlations tended to decrease slightly when student readers were excluded. Readership followed either a lognormal or a hooked power law distribution, whereas citations always followed a hooked power law, showing that the two may have underlying differences.

0.013290926 = product of:
  0.053163704 = sum of:
    0.053163704 = product of:
      0.10632741 = sum of:
        0.10632741 = weight(_text_:programming in 2043) [ClassicSimilarity], result of:
          0.10632741 = score(doc=2043,freq=2.0), product of:
            0.29361802 = queryWeight, product of:
              6.5552235 = idf(docFreq=170, maxDocs=44218)
              0.04479146 = queryNorm
            0.36212835 = fieldWeight in 2043, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.5552235 = idf(docFreq=170, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2043)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

Twitter as a potential alternative source of external links for use in webometric analysis is analyzed because of its capacity to embed hyperlinks in different tweets. Given the limitations on searching Twitter's public application programming interface (API), we used the Topsy search engine as a source for compiling tweets. To this end, we took a global sample of 200 universities and compiled all the tweets with hyperlinks to any of these institutions. Further link data was obtained from alternative sources (MajesticSEO and OpenSiteExplorer) in order to compare the results. Thereafter, various statistical tests were performed to determine the correlation between the indicators and the possibility of predicting external links from the collected tweets. The results indicate a high volume of tweets, although they are skewed by the performance of specific universities and countries. The data provided by Topsy correlated significantly with all link indicators, particularly with OpenSiteExplorer (r?=?0.769). Finally, prediction models do not provide optimum results because of high error rates. We conclude that the use of Twitter (via Topsy) as a source of hyperlinks to universities produces promising results due to its high correlation with link indicators, though limited by policies and culture regarding use and presence in social networks.

0.009102937 = product of:
  0.036411747 = sum of:
    0.036411747 = product of:
      0.072823495 = sum of:
        0.072823495 = weight(_text_:22 in 1239) [ClassicSimilarity], result of:
          0.072823495 = score(doc=1239,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.46428138 = fieldWeight in 1239, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=1239)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

18. 3.2014 19:13:22

0.0060686246 = product of:
  0.024274498 = sum of:
    0.024274498 = product of:
      0.048548996 = sum of:
        0.048548996 = weight(_text_:22 in 1431) [ClassicSimilarity], result of:
          0.048548996 = score(doc=1431,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.30952093 = fieldWeight in 1431, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=1431)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 8.2014 17:05:18

0.0060686246 = product of:
  0.024274498 = sum of:
    0.024274498 = product of:
      0.048548996 = sum of:
        0.048548996 = weight(_text_:22 in 4942) [ClassicSimilarity], result of:
          0.048548996 = score(doc=4942,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.30952093 = fieldWeight in 4942, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=4942)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

20. 1.2019 11:22:31

0.005363957 = product of:
  0.021455828 = sum of:
    0.021455828 = product of:
      0.042911656 = sum of:
        0.042911656 = weight(_text_:22 in 1291) [ClassicSimilarity], result of:
          0.042911656 = score(doc=1291,freq=4.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.27358043 = fieldWeight in 1291, 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=1291)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

This article studies the impact of differences in citation practices at the subfield, or Web of Science subject category level, using the model introduced in Crespo, Li, and Ruiz-Castillo (2013a), according to which the number of citations received by an article depends on its underlying scientific influence and the field to which it belongs. We use the same Thomson Reuters data set of about 4.4 million articles used in Crespo et al. (2013a) to analyze 22 broad fields. The main results are the following: First, when the classification system goes from 22 fields to 219 subfields the effect on citation inequality of differences in citation practices increases from ?14% at the field level to 18% at the subfield level. Second, we estimate a set of exchange rates (ERs) over a wide [660, 978] citation quantile interval to express the citation counts of articles into the equivalent counts in the all-sciences case. In the fractional case, for example, we find that in 187 of 219 subfields the ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. Third, in the fractional case the normalization of the raw data using the ERs (or subfield mean citations) as normalization factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall citation inequality. Fourth, the results in the fractional case are essentially replicated when we adopt a multiplicative approach.

0.005363957 = product of:
  0.021455828 = sum of:
    0.021455828 = product of:
      0.042911656 = sum of:
        0.042911656 = weight(_text_:22 in 1534) [ClassicSimilarity], result of:
          0.042911656 = score(doc=1534,freq=4.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.27358043 = fieldWeight in 1534, 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=1534)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

26.10.2014 20:22:22

0.005363957 = product of:
  0.021455828 = sum of:
    0.021455828 = product of:
      0.042911656 = sum of:
        0.042911656 = weight(_text_:22 in 2590) [ClassicSimilarity], result of:
          0.042911656 = score(doc=2590,freq=4.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.27358043 = fieldWeight in 2590, 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=2590)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

20. 1.2015 18:30:22
17. 9.2018 18:22:23

0.0053100465 = product of:
  0.021240186 = sum of:
    0.021240186 = product of:
      0.042480372 = sum of:
        0.042480372 = weight(_text_:22 in 4187) [ClassicSimilarity], result of:
          0.042480372 = score(doc=4187,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.2708308 = fieldWeight in 4187, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4187)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 1.2011 12:53:00

0.0053100465 = product of:
  0.021240186 = sum of:
    0.021240186 = product of:
      0.042480372 = sum of:
        0.042480372 = weight(_text_:22 in 4188) [ClassicSimilarity], result of:
          0.042480372 = score(doc=4188,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.2708308 = fieldWeight in 4188, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4188)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 1.2011 13:02:21

0.0053100465 = product of:
  0.021240186 = sum of:
    0.021240186 = product of:
      0.042480372 = sum of:
        0.042480372 = weight(_text_:22 in 900) [ClassicSimilarity], result of:
          0.042480372 = score(doc=900,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.2708308 = fieldWeight in 900, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=900)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

22. 3.2013 14:04:09

0.0053100465 = product of:
  0.021240186 = sum of:
    0.021240186 = product of:
      0.042480372 = sum of:
        0.042480372 = weight(_text_:22 in 1221) [ClassicSimilarity], result of:
          0.042480372 = score(doc=1221,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.2708308 = fieldWeight in 1221, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1221)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

18. 3.2014 18:22:21

0.0045514684 = product of:
  0.018205874 = sum of:
    0.018205874 = product of:
      0.036411747 = sum of:
        0.036411747 = weight(_text_:22 in 4000) [ClassicSimilarity], result of:
          0.036411747 = score(doc=4000,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.23214069 = fieldWeight in 4000, 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=4000)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Date

28. 9.2010 12:54:22

0.0045514684 = product of:
  0.018205874 = sum of:
    0.018205874 = product of:
      0.036411747 = sum of:
        0.036411747 = weight(_text_:22 in 4185) [ClassicSimilarity], result of:
          0.036411747 = score(doc=4185,freq=2.0), product of:
            0.15685207 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04479146 = queryNorm
            0.23214069 = fieldWeight in 4185, 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=4185)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Abstract

This article studies massive evidence about references made and citations received after a 5-year citation window by 3.7 million articles published in 1998 to 2002 in 22 scientific fields. We find that the distributions of references made and citations received share a number of basic features across sciences. Reference distributions are rather skewed to the right while citation distributions are even more highly skewed: The mean is about 20 percentage points to the right of the median, and articles with a remarkable or an outstanding number of citations represent about 9% of the total. Moreover, the existence of a power law representing the upper tail of citation distributions cannot be rejected in 17 fields whose articles represent 74.7% of the total. Contrary to the evidence in other contexts, the value of the scale parameter is above 3.5 in 13 of the 17 cases. Finally, power laws are typically small, but capture a considerable proportion of the total citations received.