Search (33 results, page 2 of 2)

0.0020296127 = product of:
  0.0040592253 = sum of:
    0.0040592253 = product of:
      0.008118451 = sum of:
        0.008118451 = weight(_text_:a in 253) [ClassicSimilarity], result of:
          0.008118451 = score(doc=253,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.15287387 = fieldWeight in 253, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=253)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In this study, we consider the structure and linking strategy of Hebrew websites of several nonprofit organizations. Because nonprofit organizations differ from commercial, educational, or governmental sectors, it is important to understand the ways they utilize the web. To the best of our knowledge, the linking structure of nonprofit organizations has not been previously studied. We surveyed websites of 54 nonprofit organizations in Israel; most of these sites have at least 100 volunteers. We compared their orientation and contents and we built their linking map. We divided the organizations into four main groups: economic aid and citizen rights organizations, health aid organizations, organizations supporting families and individuals with special needs, and organizations for women and children. We found that the number of links inside the special needs group is much higher than in the other groups. We tried to explain this behavior by considering the data obtained from the site-linking graph. The value of our results is in defining and testing a method to investigate a group of nonprofit organizations, using a case study of Israeli organizations.

Type

a

0.0020296127 = product of:
  0.0040592253 = sum of:
    0.0040592253 = product of:
      0.008118451 = sum of:
        0.008118451 = weight(_text_:a in 1258) [ClassicSimilarity], result of:
          0.008118451 = score(doc=1258,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.15287387 = fieldWeight in 1258, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=1258)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Journal-based citations are an important source of data for impact indices. However, the impact of journal articles extends beyond formal scholarly discourse. Measuring online scholarly impact calls for new indices, complementary to the older ones. This article examines a possible alternative metric source, blog posts aggregated at ResearchBlogging.org, which discuss peer-reviewed articles and provide full bibliographic references. Articles reviewed in these blogs therefore receive "blog citations." We hypothesized that articles receiving blog citations close to their publication time receive more journal citations later than the articles in the same journal published in the same year that did not receive such blog citations. Statistically significant evidence for articles published in 2009 and 2010 support this hypothesis for seven of 12 journals (58%) in 2009 and 13 of 19 journals (68%) in 2010. We suggest, based on these results, that blog citations can be used as an alternative metric source.

Type

a

0.0018909799 = product of:
  0.0037819599 = sum of:
    0.0037819599 = product of:
      0.0075639198 = sum of:
        0.0075639198 = weight(_text_:a in 6104) [ClassicSimilarity], result of:
          0.0075639198 = score(doc=6104,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14243183 = fieldWeight in 6104, 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=6104)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The World Wide Web has become one of our more important information sources, and commercial search engines are the major tools for locating information; however, it is not enough for a Web page to be indexed by the search engines-it also must rank high on relevant queries. One of the parameters involved in ranking is the number and quality of links pointing to the page, based on the assumption that links convey appreciation for a page. This article presents the results of a content analysis of the links to two top pages retrieved by Google for the query "jew" as of July 2004: the "jew" entry on the free online encyclopedia Wikipedia, and the home page of "Jew Watch," a highly anti-Semitic site. The top results for the query "jew" gained public attention in April 2004, when it was noticed that the "Jew Watch" homepage ranked number 1. From this point on, both sides engaged in "Googlebombing" (i.e., increasing the number of links pointing to these pages). The results of the study show that most of the links to these pages come from blogs and discussion links, and the number of links pointing to these pages in appreciation of their content is extremely small. These findings have implications for ranking algorithms based on link counts, and emphasize the huge difference between Web links and citations in the scientific community.

Type

a

0.0018909799 = product of:
  0.0037819599 = sum of:
    0.0037819599 = product of:
      0.0075639198 = sum of:
        0.0075639198 = weight(_text_:a in 2703) [ClassicSimilarity], result of:
          0.0075639198 = score(doc=2703,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14243183 = fieldWeight in 2703, 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=2703)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

We describe the results of an experiment designed to study user preferences for different orderings of search results from three major search engines. In the experiment, 65 users were asked to choose the best ordering from two different orderings of the same set of search results: Each pair consisted of the search engine's original top-10 ordering and a synthetic ordering created from the same top-10 results retrieved by the search engine. This process was repeated for 12 queries and nine different synthetic orderings. The results show that there is a slight overall preference for the search engines' original orderings, but the preference is rarely significant. Users' choice of the best result from each of the different orderings indicates that placement on the page (i.e., whether the result appears near the top) is the most important factor used in determining the quality of the result, not the actual content displayed in the top-10 snippets. In addition to the placement bias, we detected a small bias due to the reputation of the sites appearing in the search results.

Type

a

0.0018909799 = product of:
  0.0037819599 = sum of:
    0.0037819599 = product of:
      0.0075639198 = sum of:
        0.0075639198 = weight(_text_:a in 455) [ClassicSimilarity], result of:
          0.0075639198 = score(doc=455,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.14243183 = fieldWeight in 455, 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=455)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

This experiment studied the impact of various task phrasings on the search process. Eighty-eight searchers performed four web search tasks prescribed by the researchers. Each task was linked to an existing target web page, containing a piece of text that served as the basis for the task. A matching phrasing was a task whose wording matched the text of the target page. A nonmatching phrasing was synonymous with the matching phrasing, but had no match with the target page. Searchers received tasks for both types in English and in Hebrew. The search process was logged. The findings confirm that task phrasing shapes the search process and outcome, and also user satisfaction. Each search stage-retrieval of the target page, visiting the target page, and finding the target answer-was associated with different phenomena; for example, target page retrieval was negatively affected by persistence in search patterns (e.g., use of phrases), user-originated keywords, shorter queries, and omitting key keywords from the queries. Searchers were easily driven away from the top-ranked target pages by lower-ranked pages with title tags matching the queries. Some searchers created consistently longer queries than other searchers, regardless of the task length. Several consistent behavior patterns that characterized the Hebrew language were uncovered, including the use of keyword modifications (replacing infinitive forms with nouns), omitting prefixes and articles, and preferences for the common language. The success self-assessment also depended on whether the wording of the answer matched the task phrasing.

Type

a

0.001757696 = product of:
  0.003515392 = sum of:
    0.003515392 = product of:
      0.007030784 = sum of:
        0.007030784 = weight(_text_:a in 813) [ClassicSimilarity], result of:
          0.007030784 = score(doc=813,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.13239266 = fieldWeight in 813, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=813)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The Internet, and more specifically the World Wide Web, is quickly becoming one of our main information sources. Systematic evaluation and analysis can help us understand how this medium works, grows, and changes, and how it influences our lives and research. New approaches in informetrics can provide an appropriate means towards achieving the above goals, and towards establishing a sound theory. This paper presents a selective review of research based on the Internet, using bibliometric and informetric methods and tools. Some of these studies clearly show the applicability of bibliometric laws to the Internet, while others establish new definitions and methods based on the respective definitions for printed sources. Both informetrics and Internet research can gain from these additional methods.

Type

a

0.0016913437 = product of:
  0.0033826875 = sum of:
    0.0033826875 = product of:
      0.006765375 = sum of:
        0.006765375 = weight(_text_:a in 289) [ClassicSimilarity], result of:
          0.006765375 = score(doc=289,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.12739488 = fieldWeight in 289, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=289)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Children are increasingly using the Web. Cognitive theory tells us that directory structures are especially suited for information retrieval by children; however, empirical results show that they prefer keyword searching. One of the reasons for these findings could be that the directory structures and terminology are created by grown-ups. Using a card-sorting method and an enveloping system, we simulated the structure of a directory. Our goal was to try to understand what browsable, hierarchical subject categories children create when suggested terms are supplied and they are free to add or delete terms. Twelve groups of four children each (fourth and fifth graders) participated in our exploratory study. The initial terminology presented to the children was based on names of categories used in popular directories, in the sections on Arts, Television, Music, Cinema, and Celebrities. The children were allowed to introduce additional cards and change the terms appearing on the 61 cards. Findings show that the different groups reached reasonable consensus; the majority of the category names used by existing directories were acceptable by them and only a small minority of the terms caused confusion. Our recommendation is to include children in the design process of directories, not only in designing the interface but also in designing the content structure as well.

Type

a

0.0016913437 = product of:
  0.0033826875 = sum of:
    0.0033826875 = product of:
      0.006765375 = sum of:
        0.006765375 = weight(_text_:a in 1694) [ClassicSimilarity], result of:
          0.006765375 = score(doc=1694,freq=2.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.12739488 = fieldWeight in 1694, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.078125 = fieldNorm(doc=1694)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.0015127839 = product of:
  0.0030255679 = sum of:
    0.0030255679 = product of:
      0.0060511357 = sum of:
        0.0060511357 = weight(_text_:a in 616) [ClassicSimilarity], result of:
          0.0060511357 = score(doc=616,freq=10.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.11394546 = fieldWeight in 616, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=616)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Purpose - The objective of this paper is to characterize the changes in the rankings of the top ten results of major search engines over time and to compare the rankings between these engines. Design/methodology/approach - The papers compare rankings of the top-ten results of the search engines Google and AlltheWeb on ten identical queries over a period of three weeks. Only the top-ten results were considered, since users do not normally inspect more than the first results page returned by a search engine. The experiment was repeated twice, in October 2003 and in January 2004, in order to assess changes to the top-ten results of some of the queries during the three months interval. In order to assess the changes in the rankings, three measures were computed for each data collection point and each search engine. Findings - The findings in this paper show that the rankings of AlltheWeb were highly stable over each period, while the rankings of Google underwent constant yet minor changes, with occasional major ones. Changes over time can be explained by the dynamic nature of the web or by fluctuations in the search engines' indexes. The top-ten results of the two search engines had surprisingly low overlap. With such small overlap, the task of comparing the rankings of the two engines becomes extremely challenging. Originality/value - The paper shows that because of the abundance of information on the web, ranking search results is of extreme importance. The paper compares several measures for computing the similarity between rankings of search tools, and shows that none of the measures is fully satisfactory as a standalone measure. It also demonstrates the apparent differences in the ranking algorithms of two widely used search engines.

Type

a

0.0014647468 = product of:
  0.0029294936 = sum of:
    0.0029294936 = product of:
      0.005858987 = sum of:
        0.005858987 = weight(_text_:a in 470) [ClassicSimilarity], result of:
          0.005858987 = score(doc=470,freq=6.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.11032722 = fieldWeight in 470, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=470)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In this study, we investigate the similarities and differences between rankings of search results by users and search engines. Sixty-seven students took part in a 3-week-long experiment, during which they were asked to identify and rank the top 10 documents from the set of URLs that were retrieved by three major search engines (Google, MSN Search, and Yahoo!) for 12 selected queries. The URLs and accompanying snippets were displayed in random order, without disclosing which search engine(s) retrieved any specific URL for the query. We computed the similarity of the rankings of the users and search engines using four nonparametric correlation measures in [0,1] that complement each other. The findings show that the similarities between the users' choices and the rankings of the search engines are low. We examined the effects of the presentation order of the results, and of the thinking styles of the participants. Presentation order influences the rankings, but overall the results indicate that there is no "average user," and even if the users have the same basic knowledge of a topic, they evaluate information in their own context, which is influenced by cognitive, affective, and physical factors. This is the first large-scale experiment in which users were asked to rank the results of identical queries. The analysis of the experimental results demonstrates the potential for personalized search.

Type

a

0.0014351527 = product of:
  0.0028703054 = sum of:
    0.0028703054 = product of:
      0.005740611 = sum of:
        0.005740611 = weight(_text_:a in 3822) [ClassicSimilarity], result of:
          0.005740611 = score(doc=3822,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.10809815 = fieldWeight in 3822, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=3822)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Informetrics is a subfield of information science and it encompasses bibliometrics, scientometrics, cybermetrics, and webometrics. This encyclopedia entry provides an overview of informetrics and its subfields. In general, informetrics deals with quantitative aspects of information: its production, dissemination, evaluation, and use. Bibliometrics and scientometrics study scientific literature: papers, journals, patents, and citations; while in webometric studies the sources studied are Web pages and Web sites, and citations are replaced by hypertext links. The entry introduces major topics in informetrics: citation analysis and citation related studies, the journal impact factor, the recently defined h-index, citation databases, co-citation analysis, open access publications and its implications, informetric laws, techniques for mapping and visualization of informetric phenomena, the emerging subfields of webometrics, cybermetrics and link analysis, and research evaluation.

Type

a

0.0011959607 = product of:
  0.0023919214 = sum of:
    0.0023919214 = product of:
      0.0047838427 = sum of:
        0.0047838427 = weight(_text_:a in 1068) [ClassicSimilarity], result of:
          0.0047838427 = score(doc=1068,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.090081796 = fieldWeight in 1068, 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=1068)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The Web has become an information source for professional data gathering. Because of the vast amounts of information on almost all topics, one cannot systematically go over the whole set of results, and therefore must rely on the ordering of the results by the search engine. It is well known that search engines on the Web have low overlap in terms of coverage. In this study we measure how similar are the rankings of search engines on the overlapping results. We compare rankings of results for identical queries retrieved from several search engines. The method is based only on the set of URLs that appear in the answer sets of the engines being compared. For comparing the similarity of rankings of two search engines, the Spearman correlation coefficient is computed. When comparing more than two sets Kendall's W is used. These are well-known measures and the statistical significance of the results can be computed. The methods are demonstrated on a set of 15 queries that were submitted to four large Web search engines. The findings indicate that the large public search engines on the Web employ considerably different ranking algorithms.

Type

a

0.0011959607 = product of:
  0.0023919214 = sum of:
    0.0023919214 = product of:
      0.0047838427 = sum of:
        0.0047838427 = weight(_text_:a in 3453) [ClassicSimilarity], result of:
          0.0047838427 = score(doc=3453,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.090081796 = fieldWeight in 3453, 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=3453)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

In this article, we describe the results of an experiment designed to understand the effects of background information and social interaction on image tagging. The participants in the experiment were asked to tag 12 preselected images of Jewish cultural heritage. The users were partitioned into three groups: the first group saw only the images with no additional information whatsoever, the second group saw the images plus a short, descriptive title, and the third group saw the images, the titles, and the URL of the page in which the image appeared. In the first stage of the experiment, each user tagged the images without seeing the tags provided by the other users. In the second stage, the users saw the tags assigned by others and were encouraged to interact. Results show that after the social interaction phase, the tag sets converged and the popular tags became even more popular. Although in all cases the total number of assigned tags increased after the social interaction phase, the number of distinct tags decreased in most cases. When viewing the image only, in some cases the users were not able to correctly identify what they saw in some of the pictures, but they overcame the initial difficulties after interaction. We conclude from this experiment that social interaction may lead to convergence in tagging and that the wisdom of the crowds helps overcome the difficulties due to the lack of information.

Type

a