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  • × theme_ss:"Social tagging"
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  1. Qin, J.: Controlled semantics versus social semantics : an epistemological analysis (2008) 0.00 
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    Content
    Social semantics is more than just tags or vocabularies. It involves the users who contribute the tags, the perceptions of the world, and intentions that the tags are created for. Whilst social semantics is a valuable, massive data source for developing new knowledge systems or validating existing ones, there are also pitfalls and uncertainties. The epistemological analysis presented in this paper is an attempt to explain the differences and connections between social and controlled semantics from the perspective of knowledge theory. The epistemological connection between social and controlled semantics is particularly important: empirical knowledge can provide data source for testing the rational knowledge and rational knowledge can provide reliability and predictability. Such connection will have significant implications for future research on social and controlled semantics.
    Type
    a
  2. Corrado, E.; Moulaison, H.L.: Social tagging and communities of practice : two case studies (2008) 0.00 
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    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.
    Type
    a
  3. Kipp, M.E.I.: Tagging of biomedical articles on CiteULike : a comparison of user, author and professional indexing (2011) 0.00 
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    Type
    a
  4. Golbeck, J.; Koepfler, J.; Emmerling, B.: ¬An experimental study of social tagging behavior and image content (2011) 0.00 
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    Abstract
    Social tags have become an important tool for improving access to online resources, particularly non-text media. With the dramatic growth of user-generated content, the importance of tags is likely to grow. However, while tagging behavior is well studied, the relationship between tagging behavior and features of the media being tagged is not well understood. In this paper, we examine the relationship between tagging behavior and image type. Through a lab-based study with 51 subjects and an analysis of an online dataset of image tags, we show that there are significant differences in the number, order, and type of tags that users assign based on their past experience with an image, the type of image being tagged, and other image features. We present these results and discuss the significant implications this work has for tag-based search algorithms, tag recommendation systems, and other interface issues.
    Type
    a
  5. Vaidya, P.; Harinarayana, N.S.: ¬The comparative and analytical study of LibraryThing tags with Library of Congress Subject Headings (2016) 0.00 
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    Abstract
    The internet in its Web 2.0 version has given an opportunity among users to be participative and the chance to enhance the existing system, which makes it dynamic and collaborative. The activity of social tagging among researchers to organize the digital resources is an interesting study among information professionals. The one way of organizing the resources for future retrieval through these user-generated terms makes an interesting analysis by comparing them with professionally created controlled vocabularies. Here in this study, an attempt has been made to compare Library of Congress Subject Headings (LCSH) terms with LibraryThing social tags. In this comparative analysis, the results show that social tags can be used to enhance the metadata for information retrieval. But still, the uncontrolled nature of social tags is a concern and creates uncertainty among researchers.
    Type
    a
  6. Wang, Y.; Tai, Y.; Yang, Y.: Determination of semantic types of tags in social tagging systems (2018) 0.00 
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    Abstract
    The purpose of this paper is to determine semantic types for tags in social tagging systems. In social tagging systems, the determination of the semantic type of tags plays an important role in tag classification, increasing the semantic information of tags and establishing mapping relations between tagged resources and a normed ontology. The research reported in this paper constructs the semantic type library that is needed based on the Unified Medical Language System (UMLS) and FrameNet and determines the semantic type of selected tags that have been pretreated via direct matching using the Semantic Navigator tool, the Semantic Type Word Sense Disambiguation (STWSD) tools in UMLS, and artificial matching. And finally, we verify the feasibility of the determination of semantic type for tags by empirical analysis.
    Type
    a
  7. Spiteri, L.F.: Extending the scope of library discovery systems via hashtags (2018) 0.00 
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    Type
    a
  8. Bar-Ilan, J.; Zhitomirsky-Geffet, M.; Miller, Y.; Shoham, S.: ¬The effects of background information and social interaction on image tagging (2010) 0.00 
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    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.
    Type
    a
  9. 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 
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    Abstract
    The presence of social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using social tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems.
    Type
    a
  10. Xu, C.; Zhang, Q.: ¬The dominant factor of social tags for users' decision behavior on e-commerce websites : color or text (2019) 0.00 
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    Abstract
    Colored Tags (abbr.Tag) as a unique type of social tags is used on e-commerce websites (e.g., Taobao) to summarize the high-frequency keywords extracted from users' online reviews about products they bought before. Tag is represented inked red or green according to users' personal experiences and judgments about purchased items: red for positive comments, green for negative ones. The valence of users' emotion induced by red or green is controversial. This study firstly discovers that colored tags inked in red incite users' positive emotion (evaluations) and colored tags inked in green incite negative emotion (evaluations) using an ERP experiment, which is manifested in ERP components (e.g., N170, N2c, and LPC). There are two main features of Tag: the text of Tag (abbr. Text) and the color of Tag (abbr.Color). Our study then proves that Color (red or green) is the dominant factor in users' decision behavior compared with Text under the high cognitive load condition, while users' decision behavior is influenced by Text (positive tags or negative tags) predominately rather than by Color under the low cognitive load condition with the help of Eye tracking instrument. Those findings can help to design colored tags for recommendation systems on e-commerce websites and other online platforms.
    Type
    a
  11. Stuart, E.: Flickr: organizing and tagging images online (2019) 0.00 
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  12. Ding, Y.; Jacob, E.K.; Fried, M.; Toma, I.; Yan, E.; Foo, S.; Milojevicacute, S.: Upper tag ontology for integrating social tagging data (2010) 0.00 
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