Frequency- and Content-Based Tag Cloud Font Distribution Algorithm
The spread of Web 2.0 has caused user-generated content explosion. Users can tag resources to describe and organize
them. Tag clouds provide rough impression of relative importance of
each tag within overall cloud in order to facilitate browsing among
numerous tags and resources. The goal of our paper is to enrich
visualization of tag clouds. A font distribution algorithm has been
proposed to calculate a novel metric based on frequency and content,
and to classify among classes from this metric based on power
law distribution and percentages. The suggested algorithm has been
validated and verified on the tag cloud of a real-world thesis portal.
[1] S.A. Golder, B.A. Huberman, The Structure of Collaborative Tagging
Systems, 2005.
[2] D.H. Pink, Folksonomy, New York Times, December 11, 2005.
[3] C.C. Aggarwal, Social Network Data Analytics, 1st Edition, Springer,
ISBN 978-1-4419-8461-6, 2011.
[4] J. Sinclair, M. Cardew-Hall, The folksonomy tag cloud: when is it useful?,
Journal of Information Science, Vol. 34(1), pp. 15–29, 2008.
[5] S. Lohmann, J. Ziegler, L. Tetzlaff, Comparison of Tag Cloud Layouts:
Task-Related Performance and Visual Exploration, INTERACT, Part I,
LNCS 5726, pp. 392–404, 2009.
[6] Y. Hassan-Montero, V. Herrero-Solana, Improving Tag-Clouds as
Visual Information Retrieval Interfaces, International Conference on
Multidisciplinary Information Sciences and Technologies, Merida, Spain,
2006.
[7] J. Salonen, Self-Organizing Map Based Tag Clouds, OPAALS
Conference, 2007.
[8] A.P. Garc´ıa-Plaza, A. Zubiaga, V. Fresno, R. Mart´ınez, Social Media
Mining and Social Network Analysis: Emerging Research, Chapter 9,
Tag Cloud Reorganization: Finding Groups of Related Tags on Delicious,
pp. 140–155, 2013.
[9] kentbye’s blog, Tag Cloud Font Distribution Algorithm, http://www.
echochamberproject.com/node/247, June 24, 2005.
[10] K. Hoffman, In Search Of ... The Perfect Tag Cloud, http://files.blog-city.
com/files/J05/88284/b/insearchofperfecttagcloud.pdf.
[11] A. Clauset, C.R. Shalizi, M.E.J. Newman, Power-law distributions in
empirical data, SIAM Review, Vol. 51(4), pp. 661–703, 2009.
[12] A´ . Boga´rdi-Me´szo¨ly, A. Ro¨vid, H. Ishikawa, S. Yokoyama, Z. Va´mossy,
Tag and Topic Recommendation Systems, Acta Polytechnica Hungarica,
Vol. 10, No. 6, pp. 171–191, 2013.
[13] Thesis Portal, Faculty of Electrical Engineering and Informatics,
Budapest University of Technology and Economics, http://diplomaterv.
vik.bme.hu/en/.
[14] A. Clauset, C.R. Shalizi, M.E.J. Newman, Implementation for Article
of Power-Law Distributions in Empirical Data, http://tuvalu.santafe.edu/
∼aaronc/powerlaws/
[1] S.A. Golder, B.A. Huberman, The Structure of Collaborative Tagging
Systems, 2005.
[2] D.H. Pink, Folksonomy, New York Times, December 11, 2005.
[3] C.C. Aggarwal, Social Network Data Analytics, 1st Edition, Springer,
ISBN 978-1-4419-8461-6, 2011.
[4] J. Sinclair, M. Cardew-Hall, The folksonomy tag cloud: when is it useful?,
Journal of Information Science, Vol. 34(1), pp. 15–29, 2008.
[5] S. Lohmann, J. Ziegler, L. Tetzlaff, Comparison of Tag Cloud Layouts:
Task-Related Performance and Visual Exploration, INTERACT, Part I,
LNCS 5726, pp. 392–404, 2009.
[6] Y. Hassan-Montero, V. Herrero-Solana, Improving Tag-Clouds as
Visual Information Retrieval Interfaces, International Conference on
Multidisciplinary Information Sciences and Technologies, Merida, Spain,
2006.
[7] J. Salonen, Self-Organizing Map Based Tag Clouds, OPAALS
Conference, 2007.
[8] A.P. Garc´ıa-Plaza, A. Zubiaga, V. Fresno, R. Mart´ınez, Social Media
Mining and Social Network Analysis: Emerging Research, Chapter 9,
Tag Cloud Reorganization: Finding Groups of Related Tags on Delicious,
pp. 140–155, 2013.
[9] kentbye’s blog, Tag Cloud Font Distribution Algorithm, http://www.
echochamberproject.com/node/247, June 24, 2005.
[10] K. Hoffman, In Search Of ... The Perfect Tag Cloud, http://files.blog-city.
com/files/J05/88284/b/insearchofperfecttagcloud.pdf.
[11] A. Clauset, C.R. Shalizi, M.E.J. Newman, Power-law distributions in
empirical data, SIAM Review, Vol. 51(4), pp. 661–703, 2009.
[12] A´ . Boga´rdi-Me´szo¨ly, A. Ro¨vid, H. Ishikawa, S. Yokoyama, Z. Va´mossy,
Tag and Topic Recommendation Systems, Acta Polytechnica Hungarica,
Vol. 10, No. 6, pp. 171–191, 2013.
[13] Thesis Portal, Faculty of Electrical Engineering and Informatics,
Budapest University of Technology and Economics, http://diplomaterv.
vik.bme.hu/en/.
[14] A. Clauset, C.R. Shalizi, M.E.J. Newman, Implementation for Article
of Power-Law Distributions in Empirical Data, http://tuvalu.santafe.edu/
∼aaronc/powerlaws/
@article{"International Journal of Information, Control and Computer Sciences:67206", author = "Ágnes Bogárdi-Mészöly and Takeshi Hashimoto and Shohei Yokoyama and Hiroshi Ishikawa", title = "Frequency- and Content-Based Tag Cloud Font Distribution Algorithm", abstract = "The spread of Web 2.0 has caused user-generated content explosion. Users can tag resources to describe and organize
them. Tag clouds provide rough impression of relative importance of
each tag within overall cloud in order to facilitate browsing among
numerous tags and resources. The goal of our paper is to enrich
visualization of tag clouds. A font distribution algorithm has been
proposed to calculate a novel metric based on frequency and content,
and to classify among classes from this metric based on power
law distribution and percentages. The suggested algorithm has been
validated and verified on the tag cloud of a real-world thesis portal.
", keywords = "Tag cloud, font distribution algorithm, frequency-based, content-based, power law.", volume = "8", number = "5", pages = "784-5", }
