@inproceedings{saeed2008citation,
  abstract = {New developments in the collaborative and participatory role of Web has emerged new web based fast lane information systems like tagging and bookmarking applications. Same authors have shown elsewhere, that for same papers tags and bookmarks appear and gain volume very quickly in time as compared to citations and also hold good correlation with the citations. Studying the rank prediction models based on these systems gives advantage of gaining quick insight and localizing the highly productive and diffusible knowledge very early in time. This shows that it may be interesting to model the citation rank of a paper within the scope of a conference or journal issue, based on the bookmark counts (i-e count representing how many researchers have shown interest in a publication.) We used linear regression model for predicting citation ranks and compared both predicted citation rank models of bookmark counts and coauthor network counts for the papers of WWW06 conference. The results show that the rank prediction model based on bookmark counts is far better than the one based on coauthor network with mean absolute error for the first limited to the range of 5 and mean absolute error for second model above 18. Along with this we also compared the two bookmark prediction models out of which one was based on total citations rank as a dependent variable and the other was based on the adjusted citation rank. The citation rank was adjusted after subtracting the self and coauthor citations from total citations. The comparison reveals a significant improvement in the model and correlation after adjusting the citation rank. This may be interpreted that the bookmarking mechanisms represents the phenomenon similar to global discovery of a publication. While in the coauthor nets the papers are communicated personally and this communication or selection may not be captured within the bookmarking systems.},
  author = {Saeed, A.U. and Afzal, M.T. and Latif, A. and Tochtermann, K.},
  booktitle = {Multitopic Conference, 2008. INMIC 2008. IEEE International},
  doi = {10.1109/INMIC.2008.4777769},
  interhash = {26d1785cab132d577e377bb5bf299002},
  intrahash = {677fc89fef6c79a6a4f25cb25246e38a},
  month = dec,
  pages = {392-397},
  title = {Citation rank prediction based on bookmark counts: Exploratory case study of WWW06 papers},
  url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4777769},
  year = 2008
}

@inproceedings{doerfel2014social,
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus},
  booktitle = {Proceedings of the 23rd International World Wide Web Conference},
  interhash = {9223d6d728612c8c05a80b5edceeb78b},
  intrahash = {11fab5468dd4b4e3db662ea5e68df8e0},
  publisher = {ACM},
  series = {WWW 2014},
  title = {How Social is Social Tagging?},
  year = 2014
}

@inproceedings{doerfel2014social,
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus},
  booktitle = {Proceedings of the 23rd International World Wide Web Conference},
  interhash = {9223d6d728612c8c05a80b5edceeb78b},
  intrahash = {11fab5468dd4b4e3db662ea5e68df8e0},
  publisher = {ACM},
  series = {WWW 2014},
  title = {How Social is Social Tagging?},
  year = 2014
}

@inproceedings{koerner2010thinking,
  abstract = {Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that ‘verbose’ taggers are most useful for the emergence  of tag semantics, but also that a subset containing only 40% of the most ‘verbose’ taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing “semantic noise”, and (iii) in learning ontologies.},
  address = {Raleigh, NC, USA},
  author = {Körner, Christian and Benz, Dominik and Strohmaier, Markus and Hotho, Andreas and Stumme, Gerd},
  booktitle = {Proceedings of the 19th International World Wide Web Conference (WWW 2010)},
  interhash = {5afe6e4ce8357d8ac9698060fb438468},
  intrahash = {45f8d8f2a8251a5e988c596a5ebb3f2d},
  month = apr,
  publisher = {ACM},
  title = {Stop Thinking, start Tagging - Tag Semantics emerge from Collaborative Verbosity},
  url = {http://www.kde.cs.uni-kassel.de/benz/papers/2010/koerner2010thinking.pdf},
  year = 2010
}