Search (53 results, page 1 of 3)

0.035879306 = product of:
  0.07175861 = sum of:
    0.016202414 = weight(_text_:information in 2891) [ClassicSimilarity], result of:
      0.016202414 = score(doc=2891,freq=8.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = 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)
    0.055556197 = sum of:
      0.02331961 = weight(_text_:technology in 2891) [ClassicSimilarity], result of:
        0.02331961 = score(doc=2891,freq=2.0), product of:
          0.1417311 = queryWeight, product of:
            2.978387 = idf(docFreq=6114, maxDocs=44218)
            0.047586527 = 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)
      0.032236587 = weight(_text_:22 in 2891) [ClassicSimilarity], result of:
        0.032236587 = score(doc=2891,freq=2.0), product of:
          0.16663991 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.047586527 = 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)
  0.5 = coord(2/4)

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

0.033506516 = product of:
  0.06701303 = sum of:
    0.011456838 = weight(_text_:information in 515) [ClassicSimilarity], result of:
      0.011456838 = score(doc=515,freq=4.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = 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)
    0.055556197 = sum of:
      0.02331961 = weight(_text_:technology in 515) [ClassicSimilarity], result of:
        0.02331961 = score(doc=515,freq=2.0), product of:
          0.1417311 = queryWeight, product of:
            2.978387 = idf(docFreq=6114, maxDocs=44218)
            0.047586527 = 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)
      0.032236587 = weight(_text_:22 in 515) [ClassicSimilarity], result of:
        0.032236587 = score(doc=515,freq=2.0), product of:
          0.16663991 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.047586527 = 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)
  0.5 = coord(2/4)

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

0.033506516 = product of:
  0.06701303 = sum of:
    0.011456838 = weight(_text_:information in 5492) [ClassicSimilarity], result of:
      0.011456838 = score(doc=5492,freq=4.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = 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)
    0.055556197 = sum of:
      0.02331961 = weight(_text_:technology in 5492) [ClassicSimilarity], result of:
        0.02331961 = score(doc=5492,freq=2.0), product of:
          0.1417311 = queryWeight, product of:
            2.978387 = idf(docFreq=6114, maxDocs=44218)
            0.047586527 = 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)
      0.032236587 = weight(_text_:22 in 5492) [ClassicSimilarity], result of:
        0.032236587 = score(doc=5492,freq=2.0), product of:
          0.16663991 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.047586527 = 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)
  0.5 = coord(2/4)

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

0.016767778 = product of:
  0.033535555 = sum of:
    0.013748205 = weight(_text_:information in 3421) [ClassicSimilarity], result of:
      0.013748205 = score(doc=3421,freq=4.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = 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)
    0.019787349 = product of:
      0.039574698 = sum of:
        0.039574698 = weight(_text_:technology in 3421) [ClassicSimilarity], result of:
          0.039574698 = score(doc=3421,freq=4.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = 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)
  0.5 = coord(2/4)

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

0.015751816 = product of:
  0.031503633 = sum of:
    0.019843826 = weight(_text_:information in 3707) [ClassicSimilarity], result of:
      0.019843826 = score(doc=3707,freq=12.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = queryNorm
        0.23754507 = fieldWeight in 3707, 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=3707)
    0.011659805 = product of:
      0.02331961 = sum of:
        0.02331961 = weight(_text_:technology in 3707) [ClassicSimilarity], result of:
          0.02331961 = score(doc=3707,freq=2.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = queryNorm
            0.16453418 = fieldWeight in 3707, 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=3707)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Social tagging or collaborative tagging has become a new trend in the organization, management, and discovery of digital information. The rapid growth of shared information mostly controlled by social tags poses a new challenge for social tag-based information organization and retrieval. A plausible approach for this challenge is linking social tags to a controlled vocabulary. As an introductory step for this approach, this study investigates ways of predicting relevant subject headings for resources from social tags assigned to the resources. The prediction of subject headings was measured by five different similarity measures: tf-idf, cosine-based similarity (CoS), Jaccard similarity (or Jaccard coefficient; JS), Mutual information (MI), and information radius (IRad). Their results were compared to those by professionals. The results show that a CoS measure based on top five social tags was most effective. Inclusions of more social tags only aggravate the performance. The performance of JS is comparable to the performance of CoS while tf-idf is comparable with up to 70% less than the best performance. MI and IRad have inferior performance compared to the other methods. This study demonstrates the application of the similarity measuring techniques to the prediction of correct Library of Congress subject headings.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1658-1672

0.015751816 = product of:
  0.031503633 = sum of:
    0.019843826 = weight(_text_:information in 240) [ClassicSimilarity], result of:
      0.019843826 = score(doc=240,freq=12.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = 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)
    0.011659805 = product of:
      0.02331961 = sum of:
        0.02331961 = weight(_text_:technology in 240) [ClassicSimilarity], result of:
          0.02331961 = score(doc=240,freq=2.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = 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)
  0.5 = coord(2/4)

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

0.014887327 = product of:
  0.029774655 = sum of:
    0.01811485 = weight(_text_:information in 3453) [ClassicSimilarity], result of:
      0.01811485 = score(doc=3453,freq=10.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = 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)
    0.011659805 = product of:
      0.02331961 = sum of:
        0.02331961 = weight(_text_:technology in 3453) [ClassicSimilarity], result of:
          0.02331961 = score(doc=3453,freq=2.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = 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)
  0.5 = coord(2/4)

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

0.0147544 = product of:
  0.0295088 = sum of:
    0.00972145 = weight(_text_:information in 4371) [ClassicSimilarity], result of:
      0.00972145 = score(doc=4371,freq=2.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = 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)
    0.019787349 = product of:
      0.039574698 = sum of:
        0.039574698 = weight(_text_:technology in 4371) [ClassicSimilarity], result of:
          0.039574698 = score(doc=4371,freq=4.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = 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)
  0.5 = coord(2/4)

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

0.013973147 = product of:
  0.027946293 = sum of:
    0.011456838 = weight(_text_:information in 1046) [ClassicSimilarity], result of:
      0.011456838 = score(doc=1046,freq=4.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = queryNorm
        0.13714671 = fieldWeight in 1046, 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=1046)
    0.016489455 = product of:
      0.03297891 = sum of:
        0.03297891 = weight(_text_:technology in 1046) [ClassicSimilarity], result of:
          0.03297891 = score(doc=1046,freq=4.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = queryNorm
            0.23268649 = fieldWeight in 1046, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1046)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

This study aims to identify the categorical characteristics and usage patterns of the most popular image tags in Flickr. The "metadata usage ratio" is introduced as a means of assessing the usage of a popular tag as metadata. We also compare how popular tags are used as image tags or metadata in the Flickr general collection and the Library of Congress's photostream (LCP), also in Flickr. The Flickr popular tags in the list overall are categorically stable, and the changes that do appear reflect Flickr users' evolving technology-driven cultural experience. The popular tags in Flickr had a high number of generic objects and specific locations-related tags and were rarely at the abstract level. Conversely, the popular tags in the LCP describe more in the specific objects and time categories. Flickr users copied the Library of Congress-supplied metadata that related to specific objects or events and standard bibliographic information (e.g., author, format, time references) as popular tags in the LCP. Those popular tags related to generic objects and events showed a high metadata usage ratio, while those related to specific locations and objects showed a low image metadata usage ratio. Popular tags in Flickr appeared less frequently as image metadata when describing specific objects than specific times and locations for historical images in Flickr LCP collections. Understanding how people contribute image tags or image metadata in Flickr helps determine what users need to describe and query images, and could help improve image browsing and retrieval.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.9, S.1878-1889

0.01393111 = product of:
  0.02786222 = sum of:
    0.016202414 = weight(_text_:information in 3424) [ClassicSimilarity], result of:
      0.016202414 = score(doc=3424,freq=8.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = 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)
    0.011659805 = product of:
      0.02331961 = sum of:
        0.02331961 = weight(_text_:technology in 3424) [ClassicSimilarity], result of:
          0.02331961 = score(doc=3424,freq=2.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = 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)
  0.5 = coord(2/4)

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

0.01393111 = product of:
  0.02786222 = sum of:
    0.016202414 = weight(_text_:information in 2852) [ClassicSimilarity], result of:
      0.016202414 = score(doc=2852,freq=8.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = queryNorm
        0.19395474 = fieldWeight in 2852, 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=2852)
    0.011659805 = product of:
      0.02331961 = sum of:
        0.02331961 = weight(_text_:technology in 2852) [ClassicSimilarity], result of:
          0.02331961 = score(doc=2852,freq=2.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = queryNorm
            0.16453418 = fieldWeight in 2852, 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=2852)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Social tagging is one of the most popular methods for collecting crowd-sourced information in galleries, libraries, archives, and museums (GLAMs). However, when the number of social tags grows rapidly, using them becomes problematic and, as a result, they are often left as simply big data that cannot be used for practical purposes. To revitalize the use of this crowd-sourced information, we propose using social tags to link and cluster artworks based on an experimental study using an online collection at the Gyeonggi Museum of Modern Art (GMoMA). We view social tagging as a folksonomy, where artworks are classified by keywords of the crowd's various interpretations and one artwork can belong to several different categories simultaneously. To leverage this strength of social tags, we used a clustering method called "link communities" to detect overlapping communities in a network of artworks constructed by computing similarities between all artwork pairs. We used this framework to identify semantic relationships and clusters of similar artworks. By comparing the clustering results with curators' manual classification results, we demonstrated the potential of social tagging data for automatically clustering artworks in a way that reflects the dynamic perspectives of crowds.

Source

Journal of the Association for Information Science and Technology. 67(2016) no.4, S.885-899

0.012845755 = product of:
  0.02569151 = sum of:
    0.0140317045 = weight(_text_:information in 3452) [ClassicSimilarity], result of:
      0.0140317045 = score(doc=3452,freq=6.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = 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)
    0.011659805 = product of:
      0.02331961 = sum of:
        0.02331961 = weight(_text_:technology in 3452) [ClassicSimilarity], result of:
          0.02331961 = score(doc=3452,freq=2.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = 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)
  0.5 = coord(2/4)

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

0.012845755 = product of:
  0.02569151 = sum of:
    0.0140317045 = weight(_text_:information in 4100) [ClassicSimilarity], result of:
      0.0140317045 = score(doc=4100,freq=6.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = 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)
    0.011659805 = product of:
      0.02331961 = sum of:
        0.02331961 = weight(_text_:technology in 4100) [ClassicSimilarity], result of:
          0.02331961 = score(doc=4100,freq=2.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = 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)
  0.5 = coord(2/4)

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

0.012845755 = product of:
  0.02569151 = sum of:
    0.0140317045 = weight(_text_:information in 1091) [ClassicSimilarity], result of:
      0.0140317045 = score(doc=1091,freq=6.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = 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)
    0.011659805 = product of:
      0.02331961 = sum of:
        0.02331961 = weight(_text_:technology in 1091) [ClassicSimilarity], result of:
          0.02331961 = score(doc=1091,freq=2.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = 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)
  0.5 = coord(2/4)

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

0.012845755 = product of:
  0.02569151 = sum of:
    0.0140317045 = weight(_text_:information in 3224) [ClassicSimilarity], result of:
      0.0140317045 = score(doc=3224,freq=6.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = queryNorm
        0.16796975 = fieldWeight in 3224, 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=3224)
    0.011659805 = product of:
      0.02331961 = sum of:
        0.02331961 = weight(_text_:technology in 3224) [ClassicSimilarity], result of:
          0.02331961 = score(doc=3224,freq=2.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = queryNorm
            0.16453418 = fieldWeight in 3224, 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=3224)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

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.

Source

Journal of the Association for Information Science and Technology. 67(2016) no.12, S.3022-3035

0.011856608 = product of:
  0.023713216 = sum of:
    0.00972145 = weight(_text_:information in 3458) [ClassicSimilarity], result of:
      0.00972145 = score(doc=3458,freq=2.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = 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)
    0.013991767 = product of:
      0.027983533 = sum of:
        0.027983533 = weight(_text_:technology in 3458) [ClassicSimilarity], result of:
          0.027983533 = score(doc=3458,freq=2.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = 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)
  0.5 = coord(2/4)

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.952-963

0.011856608 = product of:
  0.023713216 = sum of:
    0.00972145 = weight(_text_:information in 4748) [ClassicSimilarity], result of:
      0.00972145 = score(doc=4748,freq=2.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = queryNorm
        0.116372846 = fieldWeight in 4748, 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=4748)
    0.013991767 = product of:
      0.027983533 = sum of:
        0.027983533 = weight(_text_:technology in 4748) [ClassicSimilarity], result of:
          0.027983533 = score(doc=4748,freq=2.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = queryNorm
            0.19744103 = fieldWeight in 4748, 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=4748)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.9, S.1750-1760

0.011856608 = product of:
  0.023713216 = sum of:
    0.00972145 = weight(_text_:information in 4984) [ClassicSimilarity], result of:
      0.00972145 = score(doc=4984,freq=2.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = queryNorm
        0.116372846 = fieldWeight in 4984, 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=4984)
    0.013991767 = product of:
      0.027983533 = sum of:
        0.027983533 = weight(_text_:technology in 4984) [ClassicSimilarity], result of:
          0.027983533 = score(doc=4984,freq=2.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = queryNorm
            0.19744103 = fieldWeight in 4984, 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=4984)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.313-322

0.011856608 = product of:
  0.023713216 = sum of:
    0.00972145 = weight(_text_:information in 1730) [ClassicSimilarity], result of:
      0.00972145 = score(doc=1730,freq=2.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = queryNorm
        0.116372846 = fieldWeight in 1730, 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=1730)
    0.013991767 = product of:
      0.027983533 = sum of:
        0.027983533 = weight(_text_:technology in 1730) [ClassicSimilarity], result of:
          0.027983533 = score(doc=1730,freq=2.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = queryNorm
            0.19744103 = fieldWeight in 1730, 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=1730)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Source

Journal of the Association for Information Science and Technology. 66(2015) no.4, S.798-817

0.011856608 = product of:
  0.023713216 = sum of:
    0.00972145 = weight(_text_:information in 2732) [ClassicSimilarity], result of:
      0.00972145 = score(doc=2732,freq=2.0), product of:
        0.083537094 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047586527 = queryNorm
        0.116372846 = fieldWeight in 2732, 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=2732)
    0.013991767 = product of:
      0.027983533 = sum of:
        0.027983533 = weight(_text_:technology in 2732) [ClassicSimilarity], result of:
          0.027983533 = score(doc=2732,freq=2.0), product of:
            0.1417311 = queryWeight, product of:
              2.978387 = idf(docFreq=6114, maxDocs=44218)
              0.047586527 = queryNorm
            0.19744103 = fieldWeight in 2732, 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=2732)
      0.5 = coord(1/2)
  0.5 = coord(2/4)

Abstract

Social tagging systems enable users to assign arbitrary tags to various digital resources. However, they face vague-meaning problems when users retrieve or present resources with the keyword-based tags. In order to solve these problems, this study takes advantage of Semantic Web technology and the topological characteristics of knowledge maps to develop a system that comprises a semantic tagging mechanism and triple-pattern and visual searching mechanisms. A field experiment was conducted to evaluate the effectiveness and user acceptance of these mechanisms in a knowledge sharing context. The results show that the semantic social tagging system is more effective than a keyword-based system. The visualized knowledge map helps users capture an overview of the knowledge domain, reduce cognitive effort for the search, and obtain more enjoyment. Traditional keyword tagging with a keyword search still has the advantage of ease of use and the users had higher intention to use it. This study also proposes directions for future development of semantic social tagging systems.

Source

Information processing and management. 48(2012) no.4, S.599-617