An epistemic dynamic model for tagging systems

Dellschaft, Klaas; Staab, Steffen

doi:10.1145/1457507.1457512

Cited by 13 publications

(26 citation statements)

References 6 publications

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“…In this section, we analyze the immediate impact of the image description on user tagging behavior by comparing the two sets of tags produced by crowdworkers with and without descriptions. To discover the nature of the difference of collected tags, we follow the literature (see Tag Properties and Methods of Tag Analysis) and focus on (a) the descriptive properties of tags (Golder & Huberman, 2006), (b) the imitation of image description (Dellschaft & Staab, 2008), (c) the similarity of tags assigned to the same resource (Dellschaft & Staab, 2012;Fu et al, 2010), (d) tag frequencies for popular tags in each setting (Lee & Schleyer, 2012), (e) tag diversity (unique tags in each setting), (f) tag specificity (Lee & Schleyer, 2012), tag length and dictionary matching (Suchanek et al, 2008), and (g) tag reusability (Nowak & Rüger, 2010;Sen et al, 2006) and resource discrimination (Dellschaft & Staab, 2012).…”

Section: The Impact Of Image Descriptions On User Tagging Behaviormentioning

confidence: 99%

The impact of image descriptions on user tagging behavior: A study of the nature and functionality of crowdsourced tags

Lin

Trattner

Brusilovsky

et al. 2014

Asso for Info Science & Tech

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Crowdsourcing has been emerging to harvest social wisdom from thousands of volunteers to perform series of tasks online. However, little research has been devoted to exploring the impact of various factors such as the content of a resource or crowdsourcing interface design to user tagging behavior. While images' titles and descriptions are frequently available in image digital libraries, it is not clear whether they should be displayed to crowdworkers engaged in tagging. This paper focuses on offering an insight to the curators of digital image libraries who face this dilemma by examining (i) how descriptions influence the user in his/her tagging behavior and (ii) how this relates to the (a) nature of the tags, (b) the emergent folksonomy, and (c) the findability of the images in the tagging system. We compared two different methods for collecting image tags from Amazon's Mechanical Turk's crowdworkers -with and without image descriptions. Several properties of generated tags were examined from different perspectives: diversity, specificity, reusability, quality, similarity, descriptiveness, etc. In addition, the study was carried out to examine the impact of image description on supporting users' information seeking with a tag cloud interface. The results showed that the properties of tags are affected by the crowdsourcing approach. Tags from the "with description" condition are more diverse and more specific than tags from the "without description" condition, while the latter has a higher tag re-use rate. A user study also revealed that different tag sets provided different support for search. Tags produced "with description" shortened the path to the target results, while tags produced without description increased user success in the search task.

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Section: The Impact Of Image Descriptions On User Tagging Behaviormentioning

confidence: 99%

The impact of image descriptions on user tagging behavior: A study of the nature and functionality of crowdsourced tags

Lin

Trattner

Brusilovsky

et al. 2014

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…In essence, Dellschaft and Staab have added (at least) two new parameters to a Yule-Simon process, and these additional parameters allows the reinforcement of existing tags to be more finely tuned. Instead of a single power law memory kernel with a single parameter τ , these additional parameters allow the modeling of "an effect that is comparable to the fat-tailed access of the Yule-Simon model with memory" while keeping tag-growth sub-linear [6]…”

Section: Adding Parameters and Background Knowledgementioning

confidence: 99%

The role of tag suggestions in folksonomies

Bollen

Halpin

2009

Proceedings of the 20th ACM Conference on Hypertext and Hypermedia

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Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource. In this paper we investigate the influence of tag suggestions on the emergence of power law distributions as a result of collaborative tag behavior. Although previous research has already shown that power laws emerge in tagging systems, the cause of why power law distributions emerge is not understood empirically. The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. This imitation behavior leads to a feedback loop in which some tags are reinforced and get more popular which is also known as the 'rich get richer' or a preferential attachment model. We present experimental results that show that the power law distribution forms regardless of whether or not tag suggestions are presented to the users. Furthermore, we show that the real effect of tag suggestions is rather subtle; the resulting power law distribution is 'compressed' if tag suggestions are given to the user, resulting in a shorter long tail and a 'compressed' top of the power law distribution. The consequences of this experiment show that tag suggestions by themselves do not account for the formation of power law distributions in tagging systems.

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“…There is a group of works that empirically support somewhat similar results in tag convergence and power law distribution of tags. Cattuto, Loreto, and Pietronero (2007), Dellschaft and Staab (2008), and Heymann, Koutrika, Garcia-Molina (2008) all demonstrate that a power law distribution, that is, a relationship between two scalar quantities of a power function form, is drawn in tag distributions over tag frequency and tag ranking. Like the work by Golder and Huberman (2006), Robu, Halpin, and Shepherd (2009) provide an analysis of how tag frequencies per tagged resource actually converge in time to stable distributions.…”

Section: Collaborative Tagging In Deliciousmentioning

confidence: 99%

Harnessing collective intelligence in social tagging using Delicious

2012

J Am Soc Inf Sci Tec

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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.

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An epistemic dynamic model for tagging systems

Cited by 13 publications

References 6 publications

The impact of image descriptions on user tagging behavior: A study of the nature and functionality of crowdsourced tags

The impact of image descriptions on user tagging behavior: A study of the nature and functionality of crowdsourced tags

The role of tag suggestions in folksonomies

Harnessing collective intelligence in social tagging using Delicious

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