Search (115 results, page 1 of 6)

  • × theme_ss:"Social tagging"
  1. Simon, J.: Interdisciplinary knowledge creation : using wikis in science (2006) 0.10
    0.10086385 = product of:
      0.1681064 = sum of:
        0.13281834 = weight(_text_:section in 2516) [ClassicSimilarity], result of:
          0.13281834 = score(doc=2516,freq=6.0), product of:
            0.26305357 = queryWeight, product of:
              5.276892 = idf(docFreq=613, maxDocs=44218)
              0.049850095 = queryNorm
            0.5049099 = fieldWeight in 2516, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.276892 = idf(docFreq=613, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2516)
        0.023073634 = weight(_text_:on in 2516) [ClassicSimilarity], result of:
          0.023073634 = score(doc=2516,freq=6.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.21044704 = fieldWeight in 2516, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2516)
        0.012214432 = product of:
          0.024428863 = sum of:
            0.024428863 = weight(_text_:technology in 2516) [ClassicSimilarity], result of:
              0.024428863 = score(doc=2516,freq=2.0), product of:
                0.14847288 = queryWeight, product of:
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.049850095 = queryNorm
                0.16453418 = fieldWeight in 2516, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2516)
          0.5 = coord(1/2)
      0.6 = coord(3/5)
    
    Abstract
    This article focuses on two aspects of knowledge generation. First, I want to explore how new knowledge is created in interdisciplinary discourses and, second, how this process might be mediated and promoted by the use of wikis. I suggest that it is the noise coming to life in (ex)changes of perspectives that enables the creation of new knowledge. In section 1-4, I am going to examine how the concepts of noise from the mathematical theory of communication (Shannon 1948) on the one hand and theories of organizational knowledge creation (cf. Nonaka 1994) on the other might help to understand the process of interdisciplinary knowledge creation. In section 5 I am going to explore the role wiki technologies can play in supporting interdisciplinary collaborations. This section is influenced by own experiences in a wiki-based interdisciplinary collaboration. It seems that even though certain features of wiki technology make it an excellent tool to externalize and combine individual knowledge leaving room for noise and at the same time documenting this process, the full benefit of wikis can only be obtained if they are embedded into a broader communication context.
  2. Chan, L.M.: Social bookmarking and subject indexing (2011) 0.08
    0.076417804 = product of:
      0.19104451 = sum of:
        0.15336542 = weight(_text_:section in 1806) [ClassicSimilarity], result of:
          0.15336542 = score(doc=1806,freq=2.0), product of:
            0.26305357 = queryWeight, product of:
              5.276892 = idf(docFreq=613, maxDocs=44218)
              0.049850095 = queryNorm
            0.58301973 = fieldWeight in 1806, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.276892 = idf(docFreq=613, maxDocs=44218)
              0.078125 = fieldNorm(doc=1806)
        0.037679087 = weight(_text_:on in 1806) [ClassicSimilarity], result of:
          0.037679087 = score(doc=1806,freq=4.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.3436586 = fieldWeight in 1806, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.078125 = fieldNorm(doc=1806)
      0.4 = coord(2/5)
    
    Series
    IFLA series on bibliographic control; vol. 42
    Source
    Subject access: preparing for the future. Conference on August 20 - 21, 2009 in Florence, the IFLA Classification and Indexing Section sponsored an IFLA satellite conference entitled "Looking at the Past and Preparing for the Future". Eds.: P. Landry et al
  3. Aagaard, H.: Social indexing at the Stockholm Public Library (2011) 0.08
    0.076417804 = product of:
      0.19104451 = sum of:
        0.15336542 = weight(_text_:section in 1807) [ClassicSimilarity], result of:
          0.15336542 = score(doc=1807,freq=2.0), product of:
            0.26305357 = queryWeight, product of:
              5.276892 = idf(docFreq=613, maxDocs=44218)
              0.049850095 = queryNorm
            0.58301973 = fieldWeight in 1807, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.276892 = idf(docFreq=613, maxDocs=44218)
              0.078125 = fieldNorm(doc=1807)
        0.037679087 = weight(_text_:on in 1807) [ClassicSimilarity], result of:
          0.037679087 = score(doc=1807,freq=4.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.3436586 = fieldWeight in 1807, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.078125 = fieldNorm(doc=1807)
      0.4 = coord(2/5)
    
    Series
    IFLA series on bibliographic control; vol. 42
    Source
    Subject access: preparing for the future. Conference on August 20 - 21, 2009 in Florence, the IFLA Classification and Indexing Section sponsored an IFLA satellite conference entitled "Looking at the Past and Preparing for the Future". Eds.: P. Landry et al
  4. Yi, K.: Harnessing collective intelligence in social tagging using Delicious (2012) 0.05
    0.053424135 = product of:
      0.08904022 = sum of:
        0.018839544 = weight(_text_:on in 515) [ClassicSimilarity], result of:
          0.018839544 = score(doc=515,freq=4.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.1718293 = fieldWeight in 515, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=515)
        0.012001811 = weight(_text_:information in 515) [ClassicSimilarity], result of:
          0.012001811 = score(doc=515,freq=4.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.13714671 = fieldWeight in 515, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=515)
        0.05819886 = sum of:
          0.024428863 = weight(_text_:technology in 515) [ClassicSimilarity], result of:
            0.024428863 = score(doc=515,freq=2.0), product of:
              0.14847288 = queryWeight, product of:
                2.978387 = idf(docFreq=6114, maxDocs=44218)
                0.049850095 = queryNorm
              0.16453418 = fieldWeight in 515, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                2.978387 = idf(docFreq=6114, maxDocs=44218)
                0.0390625 = fieldNorm(doc=515)
          0.03377 = weight(_text_:22 in 515) [ClassicSimilarity], result of:
            0.03377 = score(doc=515,freq=2.0), product of:
              0.17456654 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.049850095 = queryNorm
              0.19345059 = fieldWeight in 515, 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=515)
      0.6 = coord(3/5)
    
    Abstract
    A new collaborative approach in information organization and sharing has recently arisen, known as collaborative tagging or social indexing. A key element of collaborative tagging is the concept of collective intelligence (CI), which is a shared intelligence among all participants. This research investigates the phenomenon of social tagging in the context of CI with the aim to serve as a stepping-stone towards the mining of truly valuable social tags for web resources. This study focuses on assessing and evaluating the degree of CI embedded in social tagging over time in terms of two-parameter values, number of participants, and top frequency ranking window. Five different metrics were adopted and utilized for assessing the similarity between ranking lists: overlapList, overlapRank, Footrule, Fagin's measure, and the Inverse Rank measure. The result of this study demonstrates that a substantial degree of CI is most likely to be achieved when somewhere between the first 200 and 400 people have participated in tagging, and that a target degree of CI can be projected by controlling the two factors along with the selection of a similarity metric. The study also tests some experimental conditions for detecting social tags with high CI degree. The results of this study can be applicable to the study of filtering social tags based on CI; filtered social tags may be utilized for the metadata creation of tagged resources and possibly for the retrieval of tagged resources.
    Date
    25.12.2012 15:22:37
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.12, S.2488-2502
  5. Qin, C.; Liu, Y.; Mou, J.; Chen, J.: User adoption of a hybrid social tagging approach in an online knowledge community (2019) 0.05
    0.053424135 = product of:
      0.08904022 = sum of:
        0.018839544 = weight(_text_:on in 5492) [ClassicSimilarity], result of:
          0.018839544 = score(doc=5492,freq=4.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.1718293 = fieldWeight in 5492, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5492)
        0.012001811 = weight(_text_:information in 5492) [ClassicSimilarity], result of:
          0.012001811 = score(doc=5492,freq=4.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.13714671 = fieldWeight in 5492, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5492)
        0.05819886 = sum of:
          0.024428863 = weight(_text_:technology in 5492) [ClassicSimilarity], result of:
            0.024428863 = score(doc=5492,freq=2.0), product of:
              0.14847288 = queryWeight, product of:
                2.978387 = idf(docFreq=6114, maxDocs=44218)
                0.049850095 = queryNorm
              0.16453418 = fieldWeight in 5492, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                2.978387 = idf(docFreq=6114, maxDocs=44218)
                0.0390625 = fieldNorm(doc=5492)
          0.03377 = weight(_text_:22 in 5492) [ClassicSimilarity], result of:
            0.03377 = score(doc=5492,freq=2.0), product of:
              0.17456654 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.049850095 = queryNorm
              0.19345059 = fieldWeight in 5492, 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=5492)
      0.6 = coord(3/5)
    
    Abstract
    Purpose Online knowledge communities make great contributions to global knowledge sharing and innovation. Resource tagging approaches have been widely adopted in such communities to describe, annotate and organize knowledge resources mainly through users' participation. However, it is unclear what causes the adoption of a particular resource tagging approach. The purpose of this paper is to identify factors that drive users to use a hybrid social tagging approach. Design/methodology/approach Technology acceptance model and social cognitive theory are adopted to support an integrated model proposed in this paper. Zhihu, one of the most popular online knowledge communities in China, is taken as the survey context. A survey was conducted with a questionnaire and collected data were analyzed through structural equation model. Findings A new hybrid social resource tagging approach was refined and described. The empirical results revealed that self-efficacy, perceived usefulness (PU) and perceived ease of use exert positive effect on users' attitude. Moreover, social influence, PU and attitude impact significantly on users' intention to use a hybrid social resource tagging approach. Originality/value Theoretically, this study enriches the type of resource tagging approaches and recognizes factors influencing user adoption to use it. Regarding the practical parts, the results provide online information system providers and designers with referential strategies to improve the performance of the current tagging approaches and promote them.
    Date
    20. 1.2015 18:30:22
    Source
    Aslib journal of information management. 71(2019) no.2, S.155-175
  6. Fox, M.J.; Reece, A.: ¬The impossible decision : social tagging and Derrida's deconstructed hospitality (2013) 0.05
    0.05040239 = product of:
      0.12600598 = sum of:
        0.10735579 = weight(_text_:section in 1067) [ClassicSimilarity], result of:
          0.10735579 = score(doc=1067,freq=2.0), product of:
            0.26305357 = queryWeight, product of:
              5.276892 = idf(docFreq=613, maxDocs=44218)
              0.049850095 = queryNorm
            0.40811378 = fieldWeight in 1067, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.276892 = idf(docFreq=613, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1067)
        0.018650195 = weight(_text_:on in 1067) [ClassicSimilarity], result of:
          0.018650195 = score(doc=1067,freq=2.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.17010231 = fieldWeight in 1067, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1067)
      0.4 = coord(2/5)
    
    Footnote
    Part of a section: "Papers from the Fourth North American Symposium on Knowledge Organization, sponsored by ISKO-Canada, United States, 13-14 June, 2013, Milwaukee, Wisconsin"
  7. Kruk, S.R.; Kruk, E.; Stankiewicz, K.: Evaluation of semantic and social technologies for digital libraries (2009) 0.04
    0.042245816 = product of:
      0.07040969 = sum of:
        0.03574552 = weight(_text_:on in 3387) [ClassicSimilarity], result of:
          0.03574552 = score(doc=3387,freq=10.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.32602316 = fieldWeight in 3387, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=3387)
        0.0144021725 = weight(_text_:information in 3387) [ClassicSimilarity], result of:
          0.0144021725 = score(doc=3387,freq=4.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.16457605 = fieldWeight in 3387, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=3387)
        0.020261997 = product of:
          0.040523995 = sum of:
            0.040523995 = weight(_text_:22 in 3387) [ClassicSimilarity], result of:
              0.040523995 = score(doc=3387,freq=2.0), product of:
                0.17456654 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.049850095 = queryNorm
                0.23214069 = fieldWeight in 3387, 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=3387)
          0.5 = coord(1/2)
      0.6 = coord(3/5)
    
    Abstract
    Libraries are the tools we use to learn and to answer our questions. The quality of our work depends, among others, on the quality of the tools we use. Recent research in digital libraries is focused, on one hand on improving the infrastructure of the digital library management systems (DLMS), and on the other on improving the metadata models used to annotate collections of objects maintained by DLMS. The latter includes, among others, the semantic web and social networking technologies. Recently, the semantic web and social networking technologies are being introduced to the digital libraries domain. The expected outcome is that the overall quality of information discovery in digital libraries can be improved by employing social and semantic technologies. In this chapter we present the results of an evaluation of social and semantic end-user information discovery services for the digital libraries.
    Date
    1. 8.2010 12:35:22
  8. Ding, Y.; Jacob, E.K.; Fried, M.; Toma, I.; Yan, E.; Foo, S.; Milojevicacute, S.: Upper tag ontology for integrating social tagging data (2010) 0.04
    0.03769147 = product of:
      0.062819116 = sum of:
        0.027688364 = weight(_text_:on in 3421) [ClassicSimilarity], result of:
          0.027688364 = score(doc=3421,freq=6.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.25253648 = fieldWeight in 3421, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=3421)
        0.0144021725 = weight(_text_:information in 3421) [ClassicSimilarity], result of:
          0.0144021725 = score(doc=3421,freq=4.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.16457605 = fieldWeight in 3421, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=3421)
        0.02072858 = product of:
          0.04145716 = sum of:
            0.04145716 = weight(_text_:technology in 3421) [ClassicSimilarity], result of:
              0.04145716 = score(doc=3421,freq=4.0), product of:
                0.14847288 = queryWeight, product of:
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.049850095 = queryNorm
                0.2792238 = fieldWeight in 3421, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3421)
          0.5 = coord(1/2)
      0.6 = coord(3/5)
    
    Abstract
    Data integration and mediation have become central concerns of information technology over the past few decades. With the advent of the Web and the rapid increases in the amount of data and the number of Web documents and users, researchers have focused on enhancing the interoperability of data through the development of metadata schemes. Other researchers have looked to the wealth of metadata generated by bookmarking sites on the Social Web. While several existing ontologies have capitalized on the semantics of metadata created by tagging activities, the Upper Tag Ontology (UTO) emphasizes the structure of tagging activities to facilitate modeling of tagging data and the integration of data from different bookmarking sites as well as the alignment of tagging ontologies. UTO is described and its utility in modeling, harvesting, integrating, searching, and analyzing data is demonstrated with metadata harvested from three major social tagging systems (Delicious, Flickr, and YouTube).
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.3, S.505-521
  9. Nov, O.; Naaman, M.; Ye, C.: Analysis of participation in an online photo-sharing community : a multidimensional perspective (2010) 0.04
    0.037091162 = product of:
      0.0618186 = sum of:
        0.032631043 = weight(_text_:on in 3424) [ClassicSimilarity], result of:
          0.032631043 = score(doc=3424,freq=12.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.29761705 = fieldWeight in 3424, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3424)
        0.016973123 = weight(_text_:information in 3424) [ClassicSimilarity], result of:
          0.016973123 = score(doc=3424,freq=8.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.19395474 = fieldWeight in 3424, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3424)
        0.012214432 = product of:
          0.024428863 = sum of:
            0.024428863 = weight(_text_:technology in 3424) [ClassicSimilarity], result of:
              0.024428863 = score(doc=3424,freq=2.0), product of:
                0.14847288 = queryWeight, product of:
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.049850095 = queryNorm
                0.16453418 = fieldWeight in 3424, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3424)
          0.5 = coord(1/2)
      0.6 = coord(3/5)
    
    Abstract
    In recent years we have witnessed a significant growth of social-computing communities - online services in which users share information in various forms. As content contributions from participants are critical to the viability of these communities, it is important to understand what drives users to participate and share information with others in such settings. We extend previous literature on user contribution by studying the factors that are associated with various forms of participation in a large online photo-sharing community. Using survey and system data, we examine four different forms of participation and consider the differences between these forms. We build on theories of motivation to examine the relationship between users' participation and their motivations with respect to their tenure in the community. Amongst our findings, we identify individual motivations (both extrinsic and intrinsic) that underpin user participation, and their effects on different forms of information sharing; we show that tenure in the community does affect participation, but that this effect depends on the type of participation activity. Finally, we demonstrate that tenure in the community has a weak moderating effect on a number of motivations with regard to their effect on participation. Directions for future research, as well as implications for theory and practice, are discussed.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.3, S.555-566
  10. Ding, Y.; Jacob, E.K.; Zhang, Z.; Foo, S.; Yan, E.; George, N.L.; Guo, L.: Perspectives on social tagging (2009) 0.04
    0.036618754 = product of:
      0.061031256 = sum of:
        0.031971764 = weight(_text_:on in 3290) [ClassicSimilarity], result of:
          0.031971764 = score(doc=3290,freq=8.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.29160398 = fieldWeight in 3290, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=3290)
        0.0144021725 = weight(_text_:information in 3290) [ClassicSimilarity], result of:
          0.0144021725 = score(doc=3290,freq=4.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.16457605 = fieldWeight in 3290, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=3290)
        0.014657319 = product of:
          0.029314637 = sum of:
            0.029314637 = weight(_text_:technology in 3290) [ClassicSimilarity], result of:
              0.029314637 = score(doc=3290,freq=2.0), product of:
                0.14847288 = queryWeight, product of:
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.049850095 = queryNorm
                0.19744103 = fieldWeight in 3290, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3290)
          0.5 = coord(1/2)
      0.6 = coord(3/5)
    
    Abstract
    Social tagging is one of the major phenomena transforming the World Wide Web from a static platform into an actively shared information space. This paper addresses various aspects of social tagging, including different views on the nature of social tagging, how to make use of social tags, and how to bridge social tagging with other Web functionalities; it discusses the use of facets to facilitate browsing and searching of tagging data; and it presents an analogy between bibliometrics and tagometrics, arguing that established bibliometric methodologies can be applied to analyze tagging behavior on the Web. Based on the Upper Tag Ontology (UTO), a Web crawler was built to harvest tag data from Delicious, Flickr, and YouTube in September 2007. In total, 1.8 million objects, including bookmarks, photos, and videos, 3.1 million taggers, and 12.1 million tags were collected and analyzed. Some tagging patterns and variations are identified and discussed.
    Source
    Journal of the American Society for Information Science and Technology. 60(2009) no.12, S.2388-2401
  11. Raban, D.R.; Ronen, I.; Guy, I.: Acting or reacting? : Preferential attachment in a people-tagging system (2011) 0.04
    0.035160493 = product of:
      0.05860082 = sum of:
        0.027688364 = weight(_text_:on in 4371) [ClassicSimilarity], result of:
          0.027688364 = score(doc=4371,freq=6.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.25253648 = fieldWeight in 4371, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=4371)
        0.0101838745 = weight(_text_:information in 4371) [ClassicSimilarity], result of:
          0.0101838745 = score(doc=4371,freq=2.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.116372846 = fieldWeight in 4371, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=4371)
        0.02072858 = product of:
          0.04145716 = sum of:
            0.04145716 = weight(_text_:technology in 4371) [ClassicSimilarity], result of:
              0.04145716 = score(doc=4371,freq=4.0), product of:
                0.14847288 = queryWeight, product of:
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.049850095 = queryNorm
                0.2792238 = fieldWeight in 4371, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.046875 = fieldNorm(doc=4371)
          0.5 = coord(1/2)
      0.6 = coord(3/5)
    
    Abstract
    Social technologies tend to attract research on social structure or interaction. In this paper we analyze the individual use of a social technology, specifically an enterprise people-tagging application. We focus on active participants of the system and distinguish between users who initiate activity and those who respond to activity. This distinction is situated within the preferential attachment theory in order to examine which type of participant contributes more to the process of tagging. We analyze the usage of the people-tagging application in a snapshot representing 3 years of activity, focusing on self-tagging compared to tagging by and of others. The main findings are: (1) People who tag themselves are the most productive contributors to the system. (2) Preferential attachment saturation is reached at 12-14 tags per user. (3) The nature of participation is more significant than the number of participants for system growth. The paper concludes with theoretical and practical implications.
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.4, S.738-747
  12. Antin, J.; Earp, M.: With a little help from my friends : self-interested and prosocial behavior on MySpace Music (2010) 0.03
    0.034087777 = product of:
      0.056812957 = sum of:
        0.031971764 = weight(_text_:on in 3458) [ClassicSimilarity], result of:
          0.031971764 = score(doc=3458,freq=8.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.29160398 = fieldWeight in 3458, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.046875 = fieldNorm(doc=3458)
        0.0101838745 = weight(_text_:information in 3458) [ClassicSimilarity], result of:
          0.0101838745 = score(doc=3458,freq=2.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.116372846 = fieldWeight in 3458, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=3458)
        0.014657319 = product of:
          0.029314637 = sum of:
            0.029314637 = weight(_text_:technology in 3458) [ClassicSimilarity], result of:
              0.029314637 = score(doc=3458,freq=2.0), product of:
                0.14847288 = queryWeight, product of:
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.049850095 = queryNorm
                0.19744103 = fieldWeight in 3458, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3458)
          0.5 = coord(1/2)
      0.6 = coord(3/5)
    
    Abstract
    In this article, we explore the dynamics of prosocial and self-interested behavior among musicians on MySpace Music. MySpace Music is an important platform for social interactions and at the same time provides musicians with the opportunity for significant profit. We argue that these forces can be in tension with each other, encouraging musicians to make strategic choices about using MySpace to promote their own or others' rewards. We look for evidence of self-interested and prosocial friending strategies in the social network created by Top Friends links. We find strong evidence that individual preferences for prosocial and self-interested behavior influence friending strategies. Furthermore, our data illustrate a robust relationship between increased prominence and increased attention to others' rewards. These results shed light on how musicians manage their interactions in complex online environments and extend research on social values by demonstrating consistent preferences for prosocial or self-interested behavior in a multifaceted online setting.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.952-963
  13. Yoon, K.: Conceptual syntagmatic associations in user tagging (2012) 0.03
    0.03364549 = product of:
      0.05607581 = sum of:
        0.023073634 = weight(_text_:on in 240) [ClassicSimilarity], result of:
          0.023073634 = score(doc=240,freq=6.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.21044704 = fieldWeight in 240, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=240)
        0.020787746 = weight(_text_:information in 240) [ClassicSimilarity], result of:
          0.020787746 = score(doc=240,freq=12.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.23754507 = fieldWeight in 240, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=240)
        0.012214432 = product of:
          0.024428863 = sum of:
            0.024428863 = weight(_text_:technology in 240) [ClassicSimilarity], result of:
              0.024428863 = score(doc=240,freq=2.0), product of:
                0.14847288 = queryWeight, product of:
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.049850095 = queryNorm
                0.16453418 = fieldWeight in 240, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=240)
          0.5 = coord(1/2)
      0.6 = coord(3/5)
    
    Abstract
    This study aimed to integrate the linguistic theory of syntagmatic relations and the concept of topic and comment into an empirical analysis of user tagging. User tags on documents in a social bookmarking site reflect a user's views of an information object, which can augment the content description and provide more effective representation of information. The study presents a study of tag analysis to uncover semantic relations among tag terms implicit in user tagging. The objective was to identify the syntagmatic semantic cores of topic and comment in user tags evidenced by the meaning attached to the information object by users. The study focused on syntagmatic relations, which were based on the way in which terms were used within the information content among users. Analysis of descriptive tag terms found three primary categories of concepts: content-topic, content-comment, and context of use. The relations among terms within a group and between the content-topic and content-comment groups were determined by inferring user meaning from the user notes and from the context of the source text. Intergroup relations showed syntagmatic associations between the topic and comment, whereas intragroup relations were more general but were limited in the document context. The findings are discussed with regard to the semantics of concepts and relations in user tagging. An implication of syntagmatic relations to information search suggests that concepts can be combined by a specific association in the context of the actual use of terms.
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.5, S.923-935
  14. Huang, C.; Fu, T.; Chen, H.: Text-based video content classification for online video-sharing sites (2010) 0.03
    0.032134037 = product of:
      0.053556725 = sum of:
        0.026643137 = weight(_text_:on in 3452) [ClassicSimilarity], result of:
          0.026643137 = score(doc=3452,freq=8.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.24300331 = fieldWeight in 3452, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3452)
        0.014699157 = weight(_text_:information in 3452) [ClassicSimilarity], result of:
          0.014699157 = score(doc=3452,freq=6.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.16796975 = fieldWeight in 3452, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3452)
        0.012214432 = product of:
          0.024428863 = sum of:
            0.024428863 = weight(_text_:technology in 3452) [ClassicSimilarity], result of:
              0.024428863 = score(doc=3452,freq=2.0), product of:
                0.14847288 = queryWeight, product of:
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.049850095 = queryNorm
                0.16453418 = fieldWeight in 3452, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3452)
          0.5 = coord(1/2)
      0.6 = coord(3/5)
    
    Abstract
    With the emergence of Web 2.0, sharing personal content, communicating ideas, and interacting with other online users in Web 2.0 communities have become daily routines for online users. User-generated data from Web 2.0 sites provide rich personal information (e.g., personal preferences and interests) and can be utilized to obtain insight about cyber communities and their social networks. Many studies have focused on leveraging user-generated information to analyze blogs and forums, but few studies have applied this approach to video-sharing Web sites. In this study, we propose a text-based framework for video content classification of online-video sharing Web sites. Different types of user-generated data (e.g., titles, descriptions, and comments) were used as proxies for online videos, and three types of text features (lexical, syntactic, and content-specific features) were extracted. Three feature-based classification techniques (C4.5, Naïve Bayes, and Support Vector Machine) were used to classify videos. To evaluate the proposed framework, user-generated data from candidate videos, which were identified by searching user-given keywords on YouTube, were first collected. Then, a subset of the collected data was randomly selected and manually tagged by users as our experiment data. The experimental results showed that the proposed approach was able to classify online videos based on users' interests with accuracy rates up to 87.2%, and all three types of text features contributed to discriminating videos. Support Vector Machine outperformed C4.5 and Naïve Bayes techniques in our experiments. In addition, our case study further demonstrated that accurate video-classification results are very useful for identifying implicit cyber communities on video-sharing Web sites.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.891-906
  15. Xu, C.; Ma, B.; Chen, X.; Ma, F.: Social tagging in the scholarly world (2013) 0.03
    0.032134037 = product of:
      0.053556725 = sum of:
        0.026643137 = weight(_text_:on in 1091) [ClassicSimilarity], result of:
          0.026643137 = score(doc=1091,freq=8.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.24300331 = fieldWeight in 1091, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1091)
        0.014699157 = weight(_text_:information in 1091) [ClassicSimilarity], result of:
          0.014699157 = score(doc=1091,freq=6.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.16796975 = fieldWeight in 1091, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1091)
        0.012214432 = product of:
          0.024428863 = sum of:
            0.024428863 = weight(_text_:technology in 1091) [ClassicSimilarity], result of:
              0.024428863 = score(doc=1091,freq=2.0), product of:
                0.14847288 = queryWeight, product of:
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.049850095 = queryNorm
                0.16453418 = fieldWeight in 1091, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=1091)
          0.5 = coord(1/2)
      0.6 = coord(3/5)
    
    Abstract
    The number of research studies on social tagging has increased rapidly in the past years, but few of them highlight the characteristics and research trends in social tagging. A set of 862 academic documents relating to social tagging and published from 2005 to 2011 was thus examined using bibliometric analysis as well as the social network analysis technique. The results show that social tagging, as a research area, develops rapidly and attracts an increasing number of new entrants. There are no key authors, publication sources, or research groups that dominate the research domain of social tagging. Research on social tagging appears to focus mainly on the following three aspects: (a) components and functions of social tagging (e.g., tags, tagging objects, and tagging network), (b) taggers' behaviors and interface design, and (c) tags' organization and usage in social tagging. The trend suggest that more researchers turn to the latter two integrated with human computer interface and information retrieval, although the first aspect is the fundamental one in social tagging. Also, more studies relating to social tagging pay attention to multimedia tagging objects and not only text tagging. Previous research on social tagging was limited to a few subject domains such as information science and computer science. As an interdisciplinary research area, social tagging is anticipated to attract more researchers from different disciplines. More practical applications, especially in high-tech companies, is an encouraging research trend in social tagging.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.10, S.2045-2057
  16. Xu, C.; Zhang, Q.: ¬The dominant factor of social tags for users' decision behavior on e-commerce websites : color or text (2019) 0.03
    0.030515628 = product of:
      0.050859377 = sum of:
        0.026643137 = weight(_text_:on in 5359) [ClassicSimilarity], result of:
          0.026643137 = score(doc=5359,freq=8.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.24300331 = fieldWeight in 5359, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5359)
        0.012001811 = weight(_text_:information in 5359) [ClassicSimilarity], result of:
          0.012001811 = score(doc=5359,freq=4.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.13714671 = fieldWeight in 5359, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5359)
        0.012214432 = product of:
          0.024428863 = sum of:
            0.024428863 = weight(_text_:technology in 5359) [ClassicSimilarity], result of:
              0.024428863 = score(doc=5359,freq=2.0), product of:
                0.14847288 = queryWeight, product of:
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.049850095 = queryNorm
                0.16453418 = fieldWeight in 5359, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5359)
          0.5 = coord(1/2)
      0.6 = coord(3/5)
    
    Abstract
    Colored Tags (abbr.Tag) as a unique type of social tags is used on e-commerce websites (e.g., Taobao) to summarize the high-frequency keywords extracted from users' online reviews about products they bought before. Tag is represented inked red or green according to users' personal experiences and judgments about purchased items: red for positive comments, green for negative ones. The valence of users' emotion induced by red or green is controversial. This study firstly discovers that colored tags inked in red incite users' positive emotion (evaluations) and colored tags inked in green incite negative emotion (evaluations) using an ERP experiment, which is manifested in ERP components (e.g., N170, N2c, and LPC). There are two main features of Tag: the text of Tag (abbr. Text) and the color of Tag (abbr.Color). Our study then proves that Color (red or green) is the dominant factor in users' decision behavior compared with Text under the high cognitive load condition, while users' decision behavior is influenced by Text (positive tags or negative tags) predominately rather than by Color under the low cognitive load condition with the help of Eye tracking instrument. Those findings can help to design colored tags for recommendation systems on e-commerce websites and other online platforms.
    Footnote
    Beitrag in einem 'Special issue on neuro-information science'.
    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.9, S.942-953
  17. Choi, Y.; Syn, S.Y.: Characteristics of tagging behavior in digitized humanities online collections (2016) 0.03
    0.030068794 = product of:
      0.075171985 = sum of:
        0.016973123 = weight(_text_:information in 2891) [ClassicSimilarity], result of:
          0.016973123 = score(doc=2891,freq=8.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.19395474 = fieldWeight in 2891, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2891)
        0.05819886 = sum of:
          0.024428863 = weight(_text_:technology in 2891) [ClassicSimilarity], result of:
            0.024428863 = score(doc=2891,freq=2.0), product of:
              0.14847288 = queryWeight, product of:
                2.978387 = idf(docFreq=6114, maxDocs=44218)
                0.049850095 = queryNorm
              0.16453418 = fieldWeight in 2891, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                2.978387 = idf(docFreq=6114, maxDocs=44218)
                0.0390625 = fieldNorm(doc=2891)
          0.03377 = weight(_text_:22 in 2891) [ClassicSimilarity], result of:
            0.03377 = score(doc=2891,freq=2.0), product of:
              0.17456654 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.049850095 = queryNorm
              0.19345059 = fieldWeight in 2891, 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=2891)
      0.4 = coord(2/5)
    
    Abstract
    The purpose of this study was to examine user tags that describe digitized archival collections in the field of humanities. A collection of 8,310 tags from a digital portal (Nineteenth-Century Electronic Scholarship, NINES) was analyzed to find out what attributes of primary historical resources users described with tags. Tags were categorized to identify which tags describe the content of the resource, the resource itself, and subjective aspects (e.g., usage or emotion). The study's findings revealed that over half were content-related; tags representing opinion, usage context, or self-reference, however, reflected only a small percentage. The study further found that terms related to genre or physical format of a resource were frequently used in describing primary archival resources. It was also learned that nontextual resources had lower numbers of content-related tags and higher numbers of document-related tags than textual resources and bibliographic materials; moreover, textual resources tended to have more user-context-related tags than other resources. These findings help explain users' tagging behavior and resource interpretation in primary resources in the humanities. Such information provided through tags helps information professionals decide to what extent indexing archival and cultural resources should be done for resource description and discovery, and understand users' terminology.
    Date
    21. 4.2016 11:23:22
    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.5, S.1089-1104
    Theme
    Information Gateway
  18. Bar-Ilan, J.; Zhitomirsky-Geffet, M.; Miller, Y.; Shoham, S.: ¬The effects of background information and social interaction on image tagging (2010) 0.03
    0.030018302 = product of:
      0.0500305 = sum of:
        0.018839544 = weight(_text_:on in 3453) [ClassicSimilarity], result of:
          0.018839544 = score(doc=3453,freq=4.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.1718293 = fieldWeight in 3453, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3453)
        0.018976528 = weight(_text_:information in 3453) [ClassicSimilarity], result of:
          0.018976528 = score(doc=3453,freq=10.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.21684799 = fieldWeight in 3453, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3453)
        0.012214432 = product of:
          0.024428863 = sum of:
            0.024428863 = weight(_text_:technology in 3453) [ClassicSimilarity], result of:
              0.024428863 = score(doc=3453,freq=2.0), product of:
                0.14847288 = queryWeight, product of:
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.049850095 = queryNorm
                0.16453418 = fieldWeight in 3453, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3453)
          0.5 = coord(1/2)
      0.6 = coord(3/5)
    
    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.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.940-951
  19. Rorissa, A.: ¬A comparative study of Flickr tags and index terms in a general image collection (2010) 0.03
    0.029992333 = product of:
      0.04998722 = sum of:
        0.023073634 = weight(_text_:on in 4100) [ClassicSimilarity], result of:
          0.023073634 = score(doc=4100,freq=6.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.21044704 = fieldWeight in 4100, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4100)
        0.014699157 = weight(_text_:information in 4100) [ClassicSimilarity], result of:
          0.014699157 = score(doc=4100,freq=6.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.16796975 = fieldWeight in 4100, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4100)
        0.012214432 = product of:
          0.024428863 = sum of:
            0.024428863 = weight(_text_:technology in 4100) [ClassicSimilarity], result of:
              0.024428863 = score(doc=4100,freq=2.0), product of:
                0.14847288 = queryWeight, product of:
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.049850095 = queryNorm
                0.16453418 = fieldWeight in 4100, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  2.978387 = idf(docFreq=6114, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4100)
          0.5 = coord(1/2)
      0.6 = coord(3/5)
    
    Abstract
    Web 2.0 and social/collaborative tagging have altered the traditional roles of indexer and user. Traditional indexing tools and systems assume the top-down approach to indexing in which a trained professional is responsible for assigning index terms to information sources with a potential user in mind. However, in today's Web, end users create, organize, index, and search for images and other information sources through social tagging and other collaborative activities. One of the impediments to user-centered indexing had been the cost of soliciting user-generated index terms or tags. Social tagging of images such as those on Flickr, an online photo management and sharing application, presents an opportunity that can be seized by designers of indexing tools and systems to bridge the semantic gap between indexer terms and user vocabularies. Empirical research on the differences and similarities between user-generated tags and index terms based on controlled vocabularies has the potential to inform future design of image indexing tools and systems. Toward this end, a random sample of Flickr images and the tags assigned to them were content analyzed and compared with another sample of index terms from a general image collection using established frameworks for image attributes and contents. The results show that there is a fundamental difference between the types of tags and types of index terms used. In light of this, implications for research into and design of user-centered image indexing tools and systems are discussed.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.11, S.2230-2242
  20. Bentley, C.M.; Labelle, P.R.: ¬A comparison of social tagging designs and user participation (2008) 0.03
    0.029528571 = product of:
      0.049214285 = sum of:
        0.026104836 = weight(_text_:on in 2657) [ClassicSimilarity], result of:
          0.026104836 = score(doc=2657,freq=12.0), product of:
            0.109641045 = queryWeight, product of:
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.049850095 = queryNorm
            0.23809364 = fieldWeight in 2657, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              2.199415 = idf(docFreq=13325, maxDocs=44218)
              0.03125 = fieldNorm(doc=2657)
        0.009601449 = weight(_text_:information in 2657) [ClassicSimilarity], result of:
          0.009601449 = score(doc=2657,freq=4.0), product of:
            0.08751074 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.049850095 = queryNorm
            0.10971737 = fieldWeight in 2657, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.03125 = fieldNorm(doc=2657)
        0.0135079995 = product of:
          0.027015999 = sum of:
            0.027015999 = weight(_text_:22 in 2657) [ClassicSimilarity], result of:
              0.027015999 = score(doc=2657,freq=2.0), product of:
                0.17456654 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.049850095 = queryNorm
                0.15476047 = fieldWeight in 2657, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.03125 = fieldNorm(doc=2657)
          0.5 = coord(1/2)
      0.6 = coord(3/5)
    
    Abstract
    Social tagging empowers users to categorize content in a personally meaningful way while harnessing their potential to contribute to a collaborative construction of knowledge (Vander Wal, 2007). In addition, social tagging systems offer innovative filtering mechanisms that facilitate resource discovery and browsing (Mathes, 2004). As a result, social tags may support online communication, informal or intended learning as well as the development of online communities. The purpose of this mixed methods study is to examine how undergraduate students participate in social tagging activities in order to learn about their motivations, behaviours and practices. A better understanding of their knowledge, habits and interactions with such systems will help practitioners and developers identify important factors when designing enhancements. In the first phase of the study, students enrolled at a Canadian university completed 103 questionnaires. Quantitative results focusing on general familiarity with social tagging, frequently used Web 2.0 sites, and the purpose for engaging in social tagging activities were compiled. Eight questionnaire respondents participated in follow-up semi-structured interviews that further explored tagging practices by situating questionnaire responses within concrete experiences using popular websites such as YouTube, Facebook, Del.icio.us, and Flickr. Preliminary results of this study echo findings found in the growing literature concerning social tagging from the fields of computer science (Sen et al., 2006) and information science (Golder & Huberman, 2006; Macgregor & McCulloch, 2006). Generally, two classes of social taggers emerge: those who focus on tagging for individual purposes, and those who view tagging as a way to share or communicate meaning to others. Heavy del.icio.us users, for example, were often focused on simply organizing their own content, and seemed to be conscientiously maintaining their own personally relevant categorizations while, in many cases, placing little importance on the tags of others. Conversely, users tagging items primarily to share content preferred to use specific terms to optimize retrieval and discovery by others. Our findings should inform practitioners of how interaction design can be tailored for different tagging systems applications, and how these findings are positioned within the current debate surrounding social tagging among the resource discovery community. We also hope to direct future research in the field to place a greater importance on exploring the benefits of tagging as a socially-driven endeavour rather than uniquely as a means of managing information.
    Source
    Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

Languages

  • e 97
  • d 18

Types

  • a 101
  • el 12
  • m 7
  • s 3
  • b 2
  • More… Less…