|
|
|
| Analysis of the Power Law Characteristics in Social Tagging Systems |
| WU Zhen-yu, HU Jun, LI De-yi
|
| State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China |
|
|
|
|
Abstract Summarizing and analyzing the power law characteristics existed in social tagging systems can help understand the social tagging activities in every aspect and thus help users obtain resources with diversity and personality. In this paper, the power law characteristics of tag increasing, tag usage and tag network in social tagging systems are summarized firstly. Then the forming reasons of the power law are analyzed and the topological potential method is used to describe the social tagging process. Finally, the applications of the power law in tag visualization, automatic tagging, recommendation system and interests mining are discussed, and a personalized recommendation model was proposed. We conclude that analyzing power law characteristics can help provide users personalized information and improve the designs of social tagging systems.
|
|
Received: 22 May 2013
Published: 22 June 2026
|
|
|
|
[1] Schenkel R, Crecelius T, Kacimi M,et al. Efficient top-k querying over social-tagging networks[C]//SIGIR Conference on Research and Development in Information Retrieval. New York, USA:ACM Press, 2008:523-530.
[2] Golder S,Huberman A. The structure of collaborative tagging systems[J]. Journal of Information Science, 2006, 32（2):198-208.
[3] Zhang L, Wu X,Yu Y. Emergent semantics from folksonomies:a quantitative study[J]. Journal on Data Semantics VI, 2006, 4090:168-186.
[4] Heymann P, Ramage D, Garcia-Molina H. Social tag prediction[C]//SIGIR Conference on Research and Development in Information Retrieval.New York, USA:ACM Press, 2008:531-538.
[5] Sen S, Lam S K,Rashid A M,et al. Tagging communities, vocabulary, evolution[C]//Proceedings of the Conference on Computer Supported Cooperative Work. New York, USA:ACM Press, 2006:181-190.
[6] Newman M. Power laws, pareto distributions and zipf's law[J]. Contemporary Physics, 2005, 46:323-351.
[7] Marlow C, Naaman M,Boyd D. HT06, tagging paper, taxonomy, Flickr, academic article[C]//Proceedings of the 17th Conference on Hypertext and Hypermedia. New York, USA:ACM Press, 2006:31-40.
[8] Cattuto C. Semiotic dynamics in online social communities[J].The European Physical Journal C-Particles and Fields, 2006, 46（2):33-37.
[9] Li D F, Ding Y,Qin Z. Dynamic features of social tagging vocabulary:Delicious, Flickr and YouTube[C]//Proceedings of the International Conference on Advances in Social Network Analysis and Mining.Washington DC, USA:IEEE Computer Society, 2010:316-320.
[10] Vander Wal T. Explaining and showing broad and narrow folksonomies[EB/OL].[2013-02-10].http://www.vanderwal.net/random/entrysel.php?blog=1635,2009.
[11] Kipp M E,Campbell D G. Patterns and inconsistencies in collaborative tagging systems:an examination of tagging practices[C]//Annual General Meeting of the American Society for Information Science and Technology. Austin, TX:ACM press, 2009:1-12.
[12] Zhou T C,King I. Automobile, car, and BMW:horizontal and hierarchical approach in social tagging systems[C]//Proceeding of the 2nd ACM Workshop on Social Web Search and Mining.New York, USA:ACM press, 2009:25-32.
[13] Yin D W, Xue Z Z, Hong L J,et al. A probabilistic model for personalized tag prediction[C]//Proceedings of the 16th International Conference on Knowledge Discovery and Data Mining. New York, USA:ACM Press, 2010:959-968.
[14] Sigurbjornsson B,van Zwol R. Flickr tag recommendation based on collective knowledge[C]//Proceeding of the 17th International Conference on World Wide Web. New York, NY, USA:ACM Press, 2008:327-336.
[15] Farooq U, Kannampallil T, Song Y,et al. Evaluating tagging behavior in social bookmarking systems:metrics and design heuristics[C]//Proceedings of International Conference on Supporting Group Work. New York,USA:ACM Press, 2007:351-360.
[16] Schifanella R, Barrat A, Cattuto C,et al. Folks in folksonomies:social link prediction from shared metadata[C]//Proceedings of ACM international Conference on Web Search and Data Mining. New York, USA:ACM Press, 2010:271-280.
[17] Capocci A, Baldassarri A,Servedio V. Friendship, collaboration and semantics in Flickr:from social interaction to semantic similarity[C]. Proceedings of the International Workshop on Modeling Social Media. New York,USA:ACM Press, 2010:1-4.
[18] Lund B, Hammond T,Flack M. Social bookmarking tools （II):a case study-connotea[J]. D-Lib Magazine, 2005, 11（4):2-10.
[19] Shen K K,Wu L D. Folksonomy as a complex network[DB/OL].[2013-01-04]. http:arxiv.org/pdf/cs/0509072.pdf.
[20] Zhang Z K,Liu C. A hypergraph model of social tagging networks[J]. Journal of Statistical Mechanics:Theory and Experiment, 2010, P10005.
[21] Bouman W, de Bruin B, Hoogenboom T,et al. The realm of sociality:notes on the design of social software[C]//International Conference on Information Systems. Montreal, Canada:Association for Information Systems, 2007:154-160.
[22] Nov O, Naaman M,Chen Y. What drives content tagging:the case of photos on Flickr[C]//Proceeding of SIGCHI Conference on Human Factors in Computing Systems. New York,USA:ACM Press, 2008:1097-1100.
[23] Gupta M, Li R, Yin Z J,et al. Survey on social tagging techniques[J].SIGKDD Explorations, 2010 12（1):58-72.
[24] Simon H A. On a class of skew distribution functions[J]. Biometrika, 1955, 42（3/4):425-440.
[25] Dellschaft K,Staab S. An epistemic dynamic model for tagging systems[C]//Proceedings of the 19th ACM Conference on Hypertext and Hypermedia. New York,USA:ACM, 2008:71-80.
[26] Robu V, Halpin H,Shepherd H. Emergence of consensus and shared vocabularies in collaborative tagging systems[J]. ACM Transactions on the Web, 2009, 3（4):1-34.
[27] Halpin H, Robu V,Shepherd H. The complex dynamics of collaborative tagging[C]//Proceedings of the 16th International Conference on World Wide Web. New York, USA:ACM Press, 2007:211-220.
[28] Fu W T, Kannampallil T, Kang R,et al. Semantic imitation in social tagging[J]. ACM Transactions on Computer-Human Interaction, 2010, 17（3):1-37.
[29] 淦文燕,赫南,李德毅, 等. 一种基于拓扑势的网络社区发现方法待[J]. 软件学报, 2009, 20（8):2241-2254.
Gan Wenyan, He Nan, Li Deyi, et al. Community discovery method in networks based on topological potential[J]. Journal of Software, 2009, 20（8):2241-2254.
[30] Bollen D,Halpin H. An experimental analysis of suggestions in collaborative tagging[J]. Web Intelligence and Agent Systems:an International Journal, 2013, 11:41-53.
[31] Helic D,Strohmaier M. Building directories for social tagging systems[C]//The 20th ACM conference on Information and Knowledge Management. New York,USA:ACM Press, 2011:525-534.
[32] Li R, Bao S H, Yu Y,et al. Towards effective browsing of large scale social annotations[C]//Proceedings of the 16th International Conference on World Wide Web. New York,USA:ACM Press, 2007:943-952.
[33] Mesnage C,Carman M J. Tag navigation[C]//Proceedings of the 2nd international workshop on Social software engineering and applications.New York,USA:ACM Press, 2009:29-32.
[34] Lu Y T, Yu S, Chang T C,et al. A content-based method to enhance tag recommendation[C]//Proceedings of the 21th International Joint Conference on Artificial Intelligence. California, USA:AAAI Press, 2009:2064-2069.
[35] Sigurbjornsson B,Van Zwol R. Flickr tag recommendation based on collective knowledge[C]//Proceedings of the 17th International Conference on World Wide Web. New York,USA:ACM Press, 2008:327-336.
[36] Goarany K, Kulczycki G,Blake M B. Mining social tags to predict mashup patterns[C]//Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents.New York, USA:ACM Press, 2010:71-78.
[37] Park S T,Wei C. Pairwise preference regression for cold-start recommendation[C]//Proceedings of the 3th ACM conference on Recommender systems. New York,USA:ACM Press, 2009:21-28.
[38] Yin H Z, Cui B, Li J,et al. Challenging the long tail recommendation[J]. Proceedings of the VLDB Endowment 2012, 5（9):896-907.
[39] Zhou D, Bian J, Zheng S Y,et al. Exploring social annotations for information retrieval[C]//Proceeding of the 17th international conference on World Wide Web.New York,USA:ACM Press, 2008:715-724.
[40] Adamic L A, Lukose R M, Puniyani A R,et al. Search in power-law networks[J]. Physical Review E, 2001, 64（4):046135.
[41] Kim H N, Alkhaldi A, El-Saddik A,et al. Collaborative user modeling with user-generated tags for social recommender systems[J]. Expert Systems with Applications. 2011, 38（7):8488-8496.
[42] Schofegger K, Korner C, Singer P,et al. Learning user characteristics from social tagging behavior[C]//Proceedings of ACM Conference on Hypertext and Hypermedia. New York, USA:ACM Press, 2012:207-212.
[43] Ricci F. Context-aware music recommender systems[C]//Proceeding of the 21th International Conference on World Wide Web. New York, USA:ACM Press, 2012:865-866.
[44] Yin D W, Hong L J, Xue Z Z,et al. Temporal dynamics of user interests in tagging systems[C]//The 25th AAAI Conference on Artificial Intelligence. California, USA:AAAI press, 2011:1279-1285.
[45] Yeung C, Iwata T. Capturing implicit user influence in online social sharing[C]//Proceedings of the 21th ACM Conference on Hypertext and Hypermedia.New York, USA:ACM Press, 2010:245-254.
[46] Li X, Guo L,Zhao Y H. Tag-based social interest discovery[C]//Proceeding of the 17th International Conference on World Wide Web. New York, USA:ACM Press, 2008:675-684.
[47] Kashoob S,Caverlee J. Temporal dynamics of communities in social bookmarking systems[J]. Social Network Analysis and Mining, 2012, 2（4):387-404.
[48] Santos-Neto E, Condon D, Andrade N,et al. Reuse, temporal dynamics, interest sharing, and collaboration in social tagging systems[J]. CoRR 2013, arXiv:1301.6191.
[49] Yao J J, Cui B, Huang YX,et al. Bursty event detection from collaborative tag[J]. World Wide Web Journal, 2012, 15（2):171-195. |
|
|
|
