The dynamic features of Delicious, Flickr, and YouTube.
Uloženo v:
| Název: | The dynamic features of Delicious, Flickr, and YouTube. |
|---|---|
| Autoři: | Lin, Nan, Li, Daifeng, Ding, Ying, He, Bing, Qin, Zheng, Tang, Jie, Li, Juanzi, Dong, Tianxi |
| Zdroj: | Journal of the American Society for Information Science & Technology; Jan2012, Vol. 63 Issue 1, p139-162, 24p, 1 Diagram, 9 Charts, 16 Graphs |
| Témata: | ABSTRACTING & indexing services, ANALYSIS of variance, CATALOGING, INTERNET, LANGUAGE & languages, MOTION pictures, PHOTOGRAPHY, RESEARCH funding, SOCIAL networks, WORLD Wide Web |
| Abstrakt: | This article investigates the dynamic features of social tagging vocabularies in Delicious, Flickr, and YouTube from 2003 to 2008. Three algorithms are designed to study the macro- and micro-tag growth as well as the dynamics of taggers' activities, respectively. Moreover, we propose a Tagger Tag Resource Latent Dirichlet Allocation (TTR-LDA) model to explore the evolution of topics emerging from those social vocabularies. Our results show that (a) at the macro level, tag growth in all the three tagging systems obeys power law distribution with exponents lower than 1; at the micro level, the tag growth of popular resources in all three tagging systems follows a similar power law distribution; (b) the exponents of tag growth vary in different evolving stages of resources; (c) the growth of number of taggers associated with different popular resources presents a feature of convergence over time; (d) the active level of taggers has a positive correlation with the macro-tag growth of different tagging systems; and (e) some topics evolve into several subtopics over time while others experience relatively stable stages in which their contents do not change much, and certain groups of taggers continue their interests in them. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Databáze: | Complementary Index |
Nájsť tento článok vo Web of Science Buďte první, kdo okomentuje tento záznam!