Search (109 results, page 6 of 6)

  • × theme_ss:"Social tagging"
  1. Li, D.; Ding, Y.; Sugimoto, C.; He, B.; Tang, J.; Yan, E.; Lin, N.; Qin, Z.; Dong, T.: Modeling topic and community structure in social tagging : the TTR-LDA-Community model (2011) 0.00
    0.0021457102 = product of:
      0.008582841 = sum of:
        0.008582841 = weight(_text_:information in 4759) [ClassicSimilarity], result of:
          0.008582841 = score(doc=4759,freq=2.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.09697737 = fieldWeight in 4759, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4759)
      0.25 = coord(1/4)
    
    Source
    Journal of the American Society for Information Science and Technology. 62(2011) no.9, S.1849-1866
  2. Lin, N.; Li, D.; Ding, Y.; He, B.; Qin, Z.; Tang, J.; Li, J.; Dong, T.: ¬The dynamic features of Delicious, Flickr, and YouTube (2012) 0.00
    0.0021457102 = product of:
      0.008582841 = sum of:
        0.008582841 = weight(_text_:information in 4970) [ClassicSimilarity], result of:
          0.008582841 = score(doc=4970,freq=2.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.09697737 = fieldWeight in 4970, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4970)
      0.25 = coord(1/4)
    
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.1, S.139-162
  3. Knautz, K.; Stock, W.G.: Collective indexing of emotions in videos (2011) 0.00
    0.0021457102 = product of:
      0.008582841 = sum of:
        0.008582841 = weight(_text_:information in 295) [ClassicSimilarity], result of:
          0.008582841 = score(doc=295,freq=2.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.09697737 = fieldWeight in 295, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=295)
      0.25 = coord(1/4)
    
    Abstract
    Purpose - The object of this empirical research study is emotion, as depicted and aroused in videos. This paper seeks to answer the questions: Are users able to index such emotions consistently? Are the users' votes usable for emotional video retrieval? Design/methodology/approach - The authors worked with a controlled vocabulary for nine basic emotions (love, happiness, fun, surprise, desire, sadness, anger, disgust and fear), a slide control for adjusting the emotions' intensity, and the approach of broad folksonomies. Different users tagged the same videos. The test persons had the task of indexing the emotions of 20 videos (reprocessed clips from YouTube). The authors distinguished between emotions which were depicted in the video and those that were evoked in the user. Data were received from 776 participants and a total of 279,360 slide control values were analyzed. Findings - The consistency of the users' votes is very high; the tag distributions for the particular videos' emotions are stable. The final shape of the distributions will be reached by the tagging activities of only very few users (less than 100). By applying the approach of power tags it is possible to separate the pivotal emotions of every document - if indeed there is any feeling at all. Originality/value - This paper is one of the first steps in the new research area of emotional information retrieval (EmIR). To the authors' knowledge, it is the first research project into the collective indexing of emotions in videos.
  4. Weiand, K.; Hartl, A.; Hausmann, S.; Furche, T.; Bry, F.: Keyword-based search over semantic data (2012) 0.00
    0.0021457102 = product of:
      0.008582841 = sum of:
        0.008582841 = weight(_text_:information in 432) [ClassicSimilarity], result of:
          0.008582841 = score(doc=432,freq=2.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.09697737 = fieldWeight in 432, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=432)
      0.25 = coord(1/4)
    
    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.
  5. Syn, S.Y.; Spring, M.B.: Finding subject terms for classificatory metadata from user-generated social tags (2013) 0.00
    0.0021457102 = product of:
      0.008582841 = sum of:
        0.008582841 = weight(_text_:information in 745) [ClassicSimilarity], result of:
          0.008582841 = score(doc=745,freq=2.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.09697737 = fieldWeight in 745, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=745)
      0.25 = coord(1/4)
    
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.5, S.964-980
  6. Estrada, L.M.; Hildebrand, M.; Boer, V. de; Ossenbruggen, J. van: Time-based tags for fiction movies : comparing experts to novices using a video labeling game (2017) 0.00
    0.0021457102 = product of:
      0.008582841 = sum of:
        0.008582841 = weight(_text_:information in 3347) [ClassicSimilarity], result of:
          0.008582841 = score(doc=3347,freq=2.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.09697737 = fieldWeight in 3347, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3347)
      0.25 = coord(1/4)
    
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.2, S.348-364
  7. Good tags - bad tags : Social Tagging in der Wissensorganisation (2008) 0.00
    0.0015172462 = product of:
      0.006068985 = sum of:
        0.006068985 = weight(_text_:information in 3054) [ClassicSimilarity], result of:
          0.006068985 = score(doc=3054,freq=4.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.068573356 = fieldWeight in 3054, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.01953125 = fieldNorm(doc=3054)
      0.25 = coord(1/4)
    
    Footnote
    Enthält die Beiträge der Tagung "Social Tagging in der Wissensorganisation" am 21.-22.02.2008 am Institut für Wissensmedien (IWM) in Tübingen. Volltext unter: http://www.waxmann.com/kat/inhalt/2039Volltext.pdf. Vgl. die Rez. unter: http://sehepunkte.de/2008/11/14934.html. Rez. in: IWP 60(1009) H.4, S.246-247 (C. Wolff): "Tagging-Systeme erfreuen sich in den letzten Jahren einer ungemein großen Beliebtheit, erlauben sie dem Nutzer doch die Informationserschließung "mit eigenen Worten", also ohne Rekurs auf vorgegebene Ordnungs- und Begriffsysteme und für Medien wie Bild und Video, für die herkömmliche Verfahren des Information Retrieval (noch) versagen. Die Beherrschung der Film- und Bilderfülle, wie wir sie bei Flickr oder YouTube vorfinden, ist mit anderen Mitteln als dem intellektuellen Einsatz der Nutzer nicht vorstellbar - eine professionelle Aufbereitung angesichts der Massendaten (und ihrer zu einem großen Teil auch minderen Qualität) nicht möglich und sinnvoll. Insofern hat sich Tagging als ein probates Mittel der Erschließung herausgebildet, das dort Lücken füllen kann, wo andere Verfahren (Erschließung durch information professionals, automatische Indexierung, Erschließung durch Autoren) fehlen oder nicht anwendbar sind. Unter dem Titel "Good Tags - Bad Tags. Social Tagging in der Wissensorganisation" und der Herausgeberschaft von Birgit Gaiser, Thorsten Hampel und Stefanie Panke sind in der Reihe Medien in der Wissenschaft (Bd. 47) Beiträge eines interdisziplinären Workshops der Gesellschaft für Medien in der Wissenschaft zum Thema Tagging versammelt, der im Frühjahr 2008 am Institut für Wissensmedien in Tübingen stattgefunden hat. . . .
  8. Hammond, T.; Hannay, T.; Lund, B.; Scott, J.: Social bookmarking tools (I) : a general review (2005) 0.00
    0.0015019972 = product of:
      0.006007989 = sum of:
        0.006007989 = weight(_text_:information in 1188) [ClassicSimilarity], result of:
          0.006007989 = score(doc=1188,freq=2.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.06788416 = fieldWeight in 1188, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.02734375 = fieldNorm(doc=1188)
      0.25 = coord(1/4)
    
    Abstract
    A number of such utilities are presented here, together with an emergent new class of tools that caters more to the academic communities and that stores not only user-supplied tags, but also structured citation metadata terms wherever it is possible to glean this information from service providers. This provision of rich, structured metadata means that the user is provided with an accurate third-party identification of a document, which could be used to retrieve that document, but is also free to search on user-supplied terms so that documents of interest (or rather, references to documents) can be made discoverable and aggregated with other similar descriptions either recorded by the user or by other users. Matt Biddulph in an XML.com article last year, in which he reviews one of the better known social bookmarking tools, del.icio.us, declares that the "del.icio.us-space has three major axes: users, tags, and URLs". We fully support that assessment but choose to present this deconstruction in a reverse order. This paper thus first recaps a brief history of bookmarks, then discusses the current interest in tagging, moves on to look at certain social issues, and finally considers some of the feature sets offered by the new bookmarking tools. A general review of a number of common social bookmarking tools is presented in the annex. A companion paper describes a case study in more detail: the tool that Nature Publishing Group has made available to the scientific community as an experimental entrée into this field - Connotea; our reasons for endeavouring to provide such a utility; and experiences gained and lessons learned.
  9. Hänger, C.; Krätzsch, C.; Niemann, C.: Was vom Tagging übrig blieb : Erkenntnisse und Einsichten aus zwei Jahren Projektarbeit (2011) 0.00
    0.0010728551 = product of:
      0.0042914203 = sum of:
        0.0042914203 = weight(_text_:information in 4519) [ClassicSimilarity], result of:
          0.0042914203 = score(doc=4519,freq=2.0), product of:
            0.08850355 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.050415643 = queryNorm
            0.048488684 = fieldWeight in 4519, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.01953125 = fieldNorm(doc=4519)
      0.25 = coord(1/4)
    
    Abstract
    Das DFG-Projekt "Collaborative Tagging als neue Form der Sacherschließung" Im Oktober 2008 startete an der Universitätsbibliothek Mannheim das DFG-Projekt "Collaborative Tagging als neue Form der Sacherschließung". Über zwei Jahre hinweg wurde untersucht, welchen Beitrag das Web-2.0-Phänomen Tagging für die inhaltliche Erschließung von bisher nicht erschlossenen und somit der Nutzung kaum zugänglichen Dokumenten leisten kann. Die freie Vergabe von Schlagwörtern in Datenbanken durch die Nutzer selbst hatte sich bereits auf vielen Plattformen als äußerst effizient herausgestellt, insbesondere bei Inhalten, die einer automatischen Erschließung nicht zugänglich sind. So wurden riesige Mengen von Bildern (FlickR), Filmen (YouTube) oder Musik (LastFM) durch das Tagging recherchierbar und identifizierbar gemacht. Die Fragestellung des Projektes war entsprechend, ob und in welcher Qualität sich durch das gleiche Verfahren beispielsweise Dokumente auf Volltextservern oder in elektronischen Zeitschriften erschließen lassen. Für die Beantwortung dieser Frage, die ggf. weitreichende Konsequenzen für die Sacherschließung durch Fachreferenten haben konnte, wurde ein ganzer Komplex von Teilfragen und Teilschritten ermittelt bzw. konzipiert. Im Kern ging es aber in allen Untersuchungsschritten immer um zwei zentrale Dimensionen, nämlich um die "Akzeptanz" und um die "Qualität" des Taggings. Die Akzeptanz des Taggings wurde zunächst bei den Studierenden und Wissenschaftlern der Universität Mannheim evaluiert. Für bestimmte Zeiträume wurden Tagging-Systeme in unterschiedlichen Ausprägungen an die Recherchedienste der Universitätsbibliothek angebunden. Die Akzeptanz der einzelnen Systemausprägungen konnte dann durch die Analyse von Logfiles und durch Datenbankabfragen ausgewertet werden. Für die Qualität der Erschließung wurde auf einen Methodenmix zurückgegriffen, der im Verlauf des Projektes immer wieder an aktuelle Entwicklungen und an die Ergebnisse aus den vorangegangenen Analysen angepaßt wurde. Die Tags wurden hinsichtlich ihres Beitrags zum Information Retrieval mit Verfahren der automatischen Indexierung von Volltexten sowie mit der Erschließung durch Fachreferenten verglichen. Am Schluss sollte eine gut begründete Empfehlung stehen, wie bisher nicht erschlossene Dokumente am besten indexiert werden können: automatisch, mit Tags oder durch eine Kombination von beiden Verfahren.

Languages

  • e 91
  • d 18

Types

  • a 96
  • el 7
  • m 7
  • s 3
  • b 2
  • More… Less…