Search (45 results, page 1 of 3)

  • × theme_ss:"Social tagging"
  1. Fox, M.J.; Reece, A.: ¬The impossible decision : social tagging and Derrida's deconstructed hospitality (2013) 0.06
    0.055525415 = product of:
      0.19433895 = sum of:
        0.09897938 = weight(_text_:united in 1067) [ClassicSimilarity], result of:
          0.09897938 = score(doc=1067,freq=2.0), product of:
            0.22812355 = queryWeight, product of:
              5.6101127 = idf(docFreq=439, maxDocs=44218)
              0.04066292 = queryNorm
            0.433885 = fieldWeight in 1067, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.6101127 = idf(docFreq=439, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1067)
        0.09535956 = weight(_text_:states in 1067) [ClassicSimilarity], result of:
          0.09535956 = score(doc=1067,freq=2.0), product of:
            0.22391328 = queryWeight, product of:
              5.506572 = idf(docFreq=487, maxDocs=44218)
              0.04066292 = queryNorm
            0.42587718 = fieldWeight in 1067, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.506572 = idf(docFreq=487, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1067)
      0.2857143 = coord(2/7)
    
    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"
  2. Bentley, C.M.; Labelle, P.R.: ¬A comparison of social tagging designs and user participation (2008) 0.03
    0.027586278 = product of:
      0.09655197 = sum of:
        0.049110502 = weight(_text_:sites in 2657) [ClassicSimilarity], result of:
          0.049110502 = score(doc=2657,freq=2.0), product of:
            0.21257097 = queryWeight, product of:
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.04066292 = queryNorm
            0.23103109 = fieldWeight in 2657, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.03125 = fieldNorm(doc=2657)
        0.04744147 = sum of:
          0.025404414 = weight(_text_:design in 2657) [ClassicSimilarity], result of:
            0.025404414 = score(doc=2657,freq=2.0), product of:
              0.15288728 = queryWeight, product of:
                3.7598698 = idf(docFreq=2798, maxDocs=44218)
                0.04066292 = queryNorm
              0.16616434 = fieldWeight in 2657, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.7598698 = idf(docFreq=2798, maxDocs=44218)
                0.03125 = fieldNorm(doc=2657)
          0.022037057 = weight(_text_:22 in 2657) [ClassicSimilarity], result of:
            0.022037057 = score(doc=2657,freq=2.0), product of:
              0.14239462 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.04066292 = 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)
      0.2857143 = coord(2/7)
    
    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
  3. Matthews, B.; Jones, C.; Puzon, B.; Moon, J.; Tudhope, D.; Golub, K.; Nielsen, M.L.: ¬An evaluation of enhancing social tagging with a knowledge organization system (2010) 0.02
    0.02473638 = product of:
      0.086577326 = sum of:
        0.070699565 = weight(_text_:united in 4171) [ClassicSimilarity], result of:
          0.070699565 = score(doc=4171,freq=2.0), product of:
            0.22812355 = queryWeight, product of:
              5.6101127 = idf(docFreq=439, maxDocs=44218)
              0.04066292 = queryNorm
            0.30991787 = fieldWeight in 4171, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.6101127 = idf(docFreq=439, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4171)
        0.01587776 = product of:
          0.03175552 = sum of:
            0.03175552 = weight(_text_:design in 4171) [ClassicSimilarity], result of:
              0.03175552 = score(doc=4171,freq=2.0), product of:
                0.15288728 = queryWeight, product of:
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.04066292 = queryNorm
                0.20770542 = fieldWeight in 4171, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4171)
          0.5 = coord(1/2)
      0.2857143 = coord(2/7)
    
    Abstract
    Purpose - Traditional subject indexing and classification are considered infeasible in many digital collections. This paper seeks to investigate ways of enhancing social tagging via knowledge organization systems, with a view to improving the quality of tags for increased information discovery and retrieval performance. Design/methodology/approach - Enhanced tagging interfaces were developed for exemplar online repositories, and trials were undertaken with author and reader groups to evaluate the effectiveness of tagging augmented with control vocabulary for subject indexing of papers in online repositories. Findings - The results showed that using a knowledge organisation system to augment tagging does appear to increase the effectiveness of non-specialist users (that is, without information science training) in subject indexing. Research limitations/implications - While limited by the size and scope of the trials undertaken, these results do point to the usefulness of a mixed approach in supporting the subject indexing of online resources. Originality/value - The value of this work is as a guide to future developments in the practical support for resource indexing in online repositories.
    Footnote
    Beitrag in einem Special Issue: Content architecture: exploiting and managing diverse resources: proceedings of the first national conference of the United Kingdom chapter of the International Society for Knowedge Organization (ISKO)
  4. Chopin, K.: Finding communities : alternative viewpoints through weblogs and tagging (2008) 0.02
    0.02207597 = product of:
      0.07726589 = sum of:
        0.061388128 = weight(_text_:sites in 2341) [ClassicSimilarity], result of:
          0.061388128 = score(doc=2341,freq=2.0), product of:
            0.21257097 = queryWeight, product of:
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.04066292 = queryNorm
            0.28878886 = fieldWeight in 2341, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2341)
        0.01587776 = product of:
          0.03175552 = sum of:
            0.03175552 = weight(_text_:design in 2341) [ClassicSimilarity], result of:
              0.03175552 = score(doc=2341,freq=2.0), product of:
                0.15288728 = queryWeight, product of:
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.04066292 = queryNorm
                0.20770542 = fieldWeight in 2341, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=2341)
          0.5 = coord(1/2)
      0.2857143 = coord(2/7)
    
    Abstract
    Purpose - This paper aims to discuss and test the claim that user-based tagging allows for access to a wider variety of viewpoints than is found using other forms of online searching. Design/methodology/approach - A general overview of the nature of weblogs and user-based tagging is given, along with other relevant concepts. A case is then analyzed where viewpoints towards a specific issue are searched for using both tag searching (Technorati) and general search engine searching (Google and Google Blog Search). Findings - The claim to greater accessibility through user-based tagging is not overtly supported with these experiments. Further results for both general and tag-specific searching goes against some common assumptions about the types of content found on weblogs as opposed to more general web sites. Research limitations/implications - User-based tagging is still not widespread enough to give conclusive data for analysis. As this changes, further research in this area, using a variety of search subjects, is warranted. Originality/value - Although proponents of user-based tagging attribute many qualities to the practice, these qualities have not been properly documented or demonstrated. This paper partially rectifies this gap by testing one of the claims made, that of accessibility to alternate views, thus adding to the discussion on tagging for both researchers and other interested parties.
  5. Huang, C.; Fu, T.; Chen, H.: Text-based video content classification for online video-sharing sites (2010) 0.02
    0.019609718 = product of:
      0.13726802 = sum of:
        0.13726802 = weight(_text_:sites in 3452) [ClassicSimilarity], result of:
          0.13726802 = score(doc=3452,freq=10.0), product of:
            0.21257097 = queryWeight, product of:
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.04066292 = queryNorm
            0.6457515 = fieldWeight in 3452, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3452)
      0.14285715 = coord(1/7)
    
    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.
  6. DeZelar-Tiedman, V.: Doing the LibraryThing(TM) in an academic library catalog (2008) 0.02
    0.017179724 = product of:
      0.06012903 = sum of:
        0.049110502 = weight(_text_:sites in 2666) [ClassicSimilarity], result of:
          0.049110502 = score(doc=2666,freq=2.0), product of:
            0.21257097 = queryWeight, product of:
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.04066292 = queryNorm
            0.23103109 = fieldWeight in 2666, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.03125 = fieldNorm(doc=2666)
        0.011018529 = product of:
          0.022037057 = sum of:
            0.022037057 = weight(_text_:22 in 2666) [ClassicSimilarity], result of:
              0.022037057 = score(doc=2666,freq=2.0), product of:
                0.14239462 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.04066292 = queryNorm
                0.15476047 = fieldWeight in 2666, 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=2666)
          0.5 = coord(1/2)
      0.2857143 = coord(2/7)
    
    Abstract
    Many libraries and other cultural institutions are incorporating Web 2.0 features and enhanced metadata into their catalogs (Trant 2006). These value-added elements include those typically found in commercial and social networking sites, such as book jacket images, reviews, and usergenerated tags. One such site that libraries are exploring as a model is LibraryThing (www.librarything.com) LibraryThing is a social networking site that allows users to "catalog" their own book collections. Members can add tags and reviews to records for books, as well as engage in online discussions. In addition to its service for individuals, LibraryThing offers a feebased service to libraries, where institutions can add LibraryThing tags, recommendations, and other features to their online catalog records. This poster will present data analyzing the quality and quantity of the metadata that a large academic library would expect to gain if utilizing such a service, focusing on the overlap between titles found in the library's catalog and in LibraryThing's database, and on a comparison between the controlled subject headings in the former and the user-generated tags in the latter. During February through April 2008, a random sample of 383 titles from the University of Minnesota Libraries catalog was searched in LibraryThing. Eighty works, or 21 percent of the sample, had corresponding records available in LibraryThing. Golder and Huberman (2006) outline the advantages and disadvantages of using controlled vocabulary for subject access to information resources versus the growing trend of tags supplied by users or by content creators. Using the 80 matched records from the sample, comparisons were made between the user-supplied tags in LibraryThing (social tags) and the subject headings in the library catalog records (controlled vocabulary system). In the library records, terms from all 6XX MARC fields were used. To make a more meaningful comparison, controlled subject terms were broken down into facets according to their headings and subheadings, and each unique facet counted separately. A total of 227 subject terms were applied to the 80 catalog records, an average of 2.84 per record. In LibraryThing, 698 tags were applied to the same 80 titles, an average of 8.73 per title. The poster will further explore the relationships between the terms applied in each source, and identify where overlaps and complementary levels of access occur.
    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
  7. Choi, N.; Joo, S.: Booklovers' world : an examination of factors affecting continued usage of social cataloging sites (2016) 0.02
    0.0151896225 = product of:
      0.106327355 = sum of:
        0.106327355 = weight(_text_:sites in 3224) [ClassicSimilarity], result of:
          0.106327355 = score(doc=3224,freq=6.0), product of:
            0.21257097 = queryWeight, product of:
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.04066292 = queryNorm
            0.500197 = fieldWeight in 3224, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3224)
      0.14285715 = coord(1/7)
    
    Abstract
    Little is known about what factors influence users' continued use of social cataloging sites. This study therefore examines the impacts of key factors from theories of information systems (IS) success and sense of community (SOC) on users' continuance intention in the social cataloging context. Data collected from an online survey of 323 social cataloging users provide empirical support for the research model. The findings indicate that both information quality (IQ) and system quality (SQ) are significant predictors of satisfaction and SOC, which in turn lead to users' intentions to continue using these sites. In addition, SOC was found to affect continuance intention not only directly, but also indirectly through satisfaction. Theoretically, this study draws attention to a largely unexplored but essential area of research in the social cataloging literature and provides a fundamental basis to understand the determinants of continued social cataloging usage. From a managerial perspective, the findings suggest that social cataloging service providers should constantly focus their efforts on the quality control of their contents and system, and the enhancement of SOC among their users.
  8. Corrado, E.; Moulaison, H.L.: Social tagging and communities of practice : two case studies (2008) 0.01
    0.01488273 = product of:
      0.10417911 = sum of:
        0.10417911 = weight(_text_:sites in 2271) [ClassicSimilarity], result of:
          0.10417911 = score(doc=2271,freq=4.0), product of:
            0.21257097 = queryWeight, product of:
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.04066292 = queryNorm
            0.49009097 = fieldWeight in 2271, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.046875 = fieldNorm(doc=2271)
      0.14285715 = coord(1/7)
    
    Content
    In investigating the use of social tagging for knowledge organization and sharing, this paper reports on two case studies. Each study examines how two disparate communities of practices utilize social tagging to disseminate information to other community members in the online environment. Through the use of these tags, community members may retrieve and view relevant Web sites and online videos. The first study looks at tagging within the Code4Lib community of practice. The second study examines the use of tagging on video sharing sites used by a community of French teenagers. Uses of social tagging to share information within these communities are analyzed and discussed, and recommendations for future study are provided.
  9. Ding, Y.; Jacob, E.K.; Fried, M.; Toma, I.; Yan, E.; Foo, S.; Milojevicacute, S.: Upper tag ontology for integrating social tagging data (2010) 0.01
    0.01488273 = product of:
      0.10417911 = sum of:
        0.10417911 = weight(_text_:sites in 3421) [ClassicSimilarity], result of:
          0.10417911 = score(doc=3421,freq=4.0), product of:
            0.21257097 = queryWeight, product of:
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.04066292 = queryNorm
            0.49009097 = fieldWeight in 3421, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.046875 = fieldNorm(doc=3421)
      0.14285715 = coord(1/7)
    
    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).
  10. Tennis, J.T.: Measured time : imposing a temporal metric to classificatory structures 0.01
    0.013622794 = product of:
      0.09535956 = sum of:
        0.09535956 = weight(_text_:states in 3529) [ClassicSimilarity], result of:
          0.09535956 = score(doc=3529,freq=2.0), product of:
            0.22391328 = queryWeight, product of:
              5.506572 = idf(docFreq=487, maxDocs=44218)
              0.04066292 = queryNorm
            0.42587718 = fieldWeight in 3529, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.506572 = idf(docFreq=487, maxDocs=44218)
              0.0546875 = fieldNorm(doc=3529)
      0.14285715 = coord(1/7)
    
    Abstract
    Describes three units of time helpful for understanding and evaluating classificatory structures: long time (versions and states of classification schemes), short time (the act of indexing as repeated ritual or form), and micro-time (where stages of the interpretation process of indexing are separated out and inventoried). Concludes with a short discussion of how time and the impermanence of classification also conjures up an artistic conceptualization of indexing, and briefly uses that to question the seemingly dominant understanding of classification practice as outcome of scientific management and assembly line thought.
  11. Abbas, J.: In the margins : reflections on scribbles (2007) 0.01
    0.012277626 = product of:
      0.08594338 = sum of:
        0.08594338 = weight(_text_:sites in 659) [ClassicSimilarity], result of:
          0.08594338 = score(doc=659,freq=2.0), product of:
            0.21257097 = queryWeight, product of:
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.04066292 = queryNorm
            0.40430441 = fieldWeight in 659, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.0546875 = fieldNorm(doc=659)
      0.14285715 = coord(1/7)
    
    Abstract
    Marginalia or 'scribbling in the margins' is a means for readers to add a more in-depth level of granularity and subject representation to digital documents such as those present in social sharing environments like Flickr and del.icio.us. Social classification and social sharing sites development of user-defined descriptors or tags is discussed in the context of knowledge organization. With this position paper I present a rationale for the use of the resulting folksonomies and tag clouds being developed in these social sharing communities as a rich source of information about our users and their natural organization processes. The knowledge organization community needs to critically examine our understandings of these emerging classificatory schema and determine how best to adapt, augment, revitalize existing knowledge organization structures.
  12. Watters, C.; Nizam, N.: Knowledge organization on the Web : the emergent role of social classification (2012) 0.01
    0.012277626 = product of:
      0.08594338 = sum of:
        0.08594338 = weight(_text_:sites in 828) [ClassicSimilarity], result of:
          0.08594338 = score(doc=828,freq=2.0), product of:
            0.21257097 = queryWeight, product of:
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.04066292 = queryNorm
            0.40430441 = fieldWeight in 828, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.0546875 = fieldNorm(doc=828)
      0.14285715 = coord(1/7)
    
    Abstract
    There are close to a billion websites on the Internet with approximately 400 million users worldwide [www.internetworldstats.com]. People go to websites for a wide variety of different information tasks, from finding a restaurant to serious research. Many of the difficulties with searching the Web, as it is structured currently, can be attributed to increases to scale. The content of the Web is now so large that we only have a rough estimate of the number of sites and the range of information is extremely diverse, from blogs and photos to research articles and news videos.
  13. Oudenaar, H.; Bullard, J.: NOT A BOOK : goodreads and the risks of social cataloging with insufficient direction (2024) 0.01
    0.012277626 = product of:
      0.08594338 = sum of:
        0.08594338 = weight(_text_:sites in 1156) [ClassicSimilarity], result of:
          0.08594338 = score(doc=1156,freq=2.0), product of:
            0.21257097 = queryWeight, product of:
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.04066292 = queryNorm
            0.40430441 = fieldWeight in 1156, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1156)
      0.14285715 = coord(1/7)
    
    Abstract
    Social cataloging websites, such as Goodreads, LibraryThing, and StoryGraph are widely popular with individuals who want to track their reading and read reviews. Goodreads is one of the most popular sites with 90 million registered users as of 2019. This paper studies a Goodreads cataloging rule, NOT A BOOK (NAB), through which users designate items as invalid to the site's scope while preserving some of their metadata. By reviewing NAB, we identify thirteen types of invalid items. We go on to discuss how these item types unevenly reflect the rule itself and the emergence of a "non-book" sense through social cataloging.
  14. Konkova, E.; Göker, A.; Butterworth, R.; MacFarlane, A.: Social tagging: exploring the image, the tags, and the game (2014) 0.01
    0.012119926 = product of:
      0.08483948 = sum of:
        0.08483948 = weight(_text_:united in 1370) [ClassicSimilarity], result of:
          0.08483948 = score(doc=1370,freq=2.0), product of:
            0.22812355 = queryWeight, product of:
              5.6101127 = idf(docFreq=439, maxDocs=44218)
              0.04066292 = queryNorm
            0.37190145 = fieldWeight in 1370, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.6101127 = idf(docFreq=439, maxDocs=44218)
              0.046875 = fieldNorm(doc=1370)
      0.14285715 = coord(1/7)
    
    Content
    Papers from the ISKO-UK Biennial Conference, "Knowledge Organization: Pushing the Boundaries," United Kingdom, 8-9 July, 2013, London.
  15. Weiand, K.; Hartl, A.; Hausmann, S.; Furche, T.; Bry, F.: Keyword-based search over semantic data (2012) 0.01
    0.008769733 = product of:
      0.061388128 = sum of:
        0.061388128 = weight(_text_:sites in 432) [ClassicSimilarity], result of:
          0.061388128 = score(doc=432,freq=2.0), product of:
            0.21257097 = queryWeight, product of:
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.04066292 = queryNorm
            0.28878886 = fieldWeight in 432, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.0390625 = fieldNorm(doc=432)
      0.14285715 = coord(1/7)
    
    Abstract
    For a long while, the creation of Web content required at least basic knowledge of Web technologies, meaning that for many Web users, the Web was de facto a read-only medium. This changed with the arrival of the "social Web," when Web applications started to allow users to publish Web content without technological expertise. Here, content creation is often an inclusive, iterative, and interactive process. Examples of social Web applications include blogs, social networking sites, as well as many specialized applications, for example, for saving and sharing bookmarks and publishing photos. Social semantic Web applications are social Web applications in which knowledge is expressed not only in the form of text and multimedia but also through informal to formal annotations that describe, reflect, and enhance the content. These annotations often take the shape of RDF graphs backed by ontologies, but less formal annotations such as free-form tags or tags from a controlled vocabulary may also be available. Wikis are one example of social Web applications for collecting and sharing knowledge. They allow users to easily create and edit documents, so-called wiki pages, using a Web browser. The pages in a wiki are often heavily interlinked, which makes it easy to find related information and browse the content.
  16. Kipp, M.E.; Beak, J.; Choi, I.: Motivations and intentions of flickr users in enriching flick records for Library of Congress photos (2017) 0.01
    0.008769733 = product of:
      0.061388128 = sum of:
        0.061388128 = weight(_text_:sites in 3828) [ClassicSimilarity], result of:
          0.061388128 = score(doc=3828,freq=2.0), product of:
            0.21257097 = queryWeight, product of:
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.04066292 = queryNorm
            0.28878886 = fieldWeight in 3828, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3828)
      0.14285715 = coord(1/7)
    
    Abstract
    The purpose of this study is to understand users' motivations and intentions in the use of institutional collections on social tagging sites. Previous social tagging studies have collected social tagging data and analyzed how tagging functions as a tool to organize and retrieve information. Many studies focused on the patterns of tagging rather than the users' perspectives. To provide a more comprehensive picture of users' social tagging activities in institutional collections, and how this compares to social tagging in a more personal context, we collected data from social tagging users by surveying 7,563 participants in the Library of Congress's Flickr Collection. We asked users to describe their motivations for activities within the LC Flickr Collection in their own words using open-ended questions. As a result, we identified 11 motivations using a bottom-up, open-coding approach: affective reactions, opinion on photo, interest in subject, contribution to description, knowledge sharing, improving findability, social network, appreciation, personal use, and personal relationship. Our study revealed that affective or emotional reactions play a critical role in the use of social tagging of institutional collections by comparing our findings to existing frameworks for tagging motivations. We also examined the relationships between participants' occupations and our 11 motivations.
  17. Social tagging in a linked data environment. Edited by Diane Rasmussen Pennington and Louise F. Spiteri. London, UK: Facet Publishing, 2018. 240 pp. £74.95 (paperback). (ISBN 9781783303380) (2019) 0.01
    0.008769733 = product of:
      0.061388128 = sum of:
        0.061388128 = weight(_text_:sites in 101) [ClassicSimilarity], result of:
          0.061388128 = score(doc=101,freq=2.0), product of:
            0.21257097 = queryWeight, product of:
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.04066292 = queryNorm
            0.28878886 = fieldWeight in 101, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.0390625 = fieldNorm(doc=101)
      0.14285715 = coord(1/7)
    
    Abstract
    Social tagging, hashtags, and geotags are used across a variety of platforms (Twitter, Facebook, Tumblr, WordPress, Instagram) in different countries and cultures. This book, representing researchers and practitioners across different information professions, explores how social tags can link content across a variety of environments. Most studies of social tagging have tended to focus on applications like library catalogs, blogs, and social bookmarking sites. This book, in setting out a theoretical background and the use of a series of case studies, explores the role of hashtags as a form of linked data?without the complex implementation of RDF and other Semantic Web technologies.
  18. Qin, C.; Liu, Y.; Mou, J.; Chen, J.: User adoption of a hybrid social tagging approach in an online knowledge community (2019) 0.01
    0.008471692 = product of:
      0.05930184 = sum of:
        0.05930184 = sum of:
          0.03175552 = weight(_text_:design in 5492) [ClassicSimilarity], result of:
            0.03175552 = score(doc=5492,freq=2.0), product of:
              0.15288728 = queryWeight, product of:
                3.7598698 = idf(docFreq=2798, maxDocs=44218)
                0.04066292 = queryNorm
              0.20770542 = fieldWeight in 5492, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.7598698 = idf(docFreq=2798, maxDocs=44218)
                0.0390625 = fieldNorm(doc=5492)
          0.027546322 = weight(_text_:22 in 5492) [ClassicSimilarity], result of:
            0.027546322 = score(doc=5492,freq=2.0), product of:
              0.14239462 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.04066292 = 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)
      0.14285715 = coord(1/7)
    
    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
  19. Heckner, M.; Mühlbacher, S.; Wolff, C.: Tagging tagging : a classification model for user keywords in scientific bibliography management systems (2007) 0.01
    0.007015786 = product of:
      0.049110502 = sum of:
        0.049110502 = weight(_text_:sites in 533) [ClassicSimilarity], result of:
          0.049110502 = score(doc=533,freq=2.0), product of:
            0.21257097 = queryWeight, product of:
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.04066292 = queryNorm
            0.23103109 = fieldWeight in 533, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.227637 = idf(docFreq=644, maxDocs=44218)
              0.03125 = fieldNorm(doc=533)
      0.14285715 = coord(1/7)
    
    Abstract
    Recently, a growing amount of systems that allow personal content annotation (tagging) are being created, ranging from personal sites for organising bookmarks (del.icio.us), photos (flickr.com) or videos (video.google.com, youtube.com) to systems for managing bibliographies for scientific research projects (citeulike.org, connotea.org). Simultaneously, a debate on the pro and cons of allowing users to add personal keywords to digital content has arisen. One recurrent point-of-discussion is whether tagging can solve the well-known vocabulary problem: In order to support successful retrieval in complex environments, it is necessary to index an object with a variety of aliases (cf. Furnas 1987). In this spirit, social tagging enhances the pool of rigid, traditional keywording by adding user-created retrieval vocabularies. Furthermore, tagging goes beyond simple personal content-based keywords by providing meta-keywords like funny or interesting that "identify qualities or characteristics" (Golder and Huberman 2006, Kipp and Campbell 2006, Kipp 2007, Feinberg 2006, Kroski 2005). Contrarily, tagging systems are claimed to lead to semantic difficulties that may hinder the precision and recall of tagging systems (e.g. the polysemy problem, cf. Marlow 2006, Lakoff 2005, Golder and Huberman 2006). Empirical research on social tagging is still rare and mostly from a computer linguistics or librarian point-of-view (Voß 2007) which focus either on the automatic statistical analyses of large data sets, or intellectually inspect single cases of tag usage: Some scientists studied the evolution of tag vocabularies and tag distribution in specific systems (Golder and Huberman 2006, Hammond 2005). Others concentrate on tagging behaviour and tagger characteristics in collaborative systems. (Hammond 2005, Kipp and Campbell 2007, Feinberg 2006, Sen 2006). However, little research has been conducted on the functional and linguistic characteristics of tags.1 An analysis of these patterns could show differences between user wording and conventional keywording. In order to provide a reasonable basis for comparison, a classification system for existing tags is needed.
  20. Rorissa, A.: ¬A comparative study of Flickr tags and index terms in a general image collection (2010) 0.00
    0.003207792 = product of:
      0.022454543 = sum of:
        0.022454543 = product of:
          0.044909086 = sum of:
            0.044909086 = weight(_text_:design in 4100) [ClassicSimilarity], result of:
              0.044909086 = score(doc=4100,freq=4.0), product of:
                0.15288728 = queryWeight, product of:
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.04066292 = queryNorm
                0.29373983 = fieldWeight in 4100, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.7598698 = idf(docFreq=2798, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4100)
          0.5 = coord(1/2)
      0.14285715 = coord(1/7)
    
    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.

Languages

  • e 42
  • d 3
  • More… Less…

Types

  • a 40
  • el 3
  • m 3
  • b 2
  • s 1
  • More… Less…