research-article

Analyzing user behavior across social sharing environments

Authors:

Pasquale de Meo,

Emilio Ferrara,

Geert-Jan HoubenAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 5, Issue 1

Article No.: 14, Pages 1 - 31

https://doi.org/10.1145/2535526

Published: 03 January 2014 Publication History

Abstract

In this work we present an in-depth analysis of the user behaviors on different Social Sharing systems. We consider three popular platforms, Flickr, Delicious and StumbleUpon, and, by combining techniques from social network analysis with techniques from semantic analysis, we characterize the tagging behavior as well as the tendency to create friendship relationships of the users of these platforms. The aim of our investigation is to see if (and how) the features and goals of a given Social Sharing system reflect on the behavior of its users and, moreover, if there exists a correlation between the social and tagging behavior of the users. We report our findings in terms of the characteristics of user profiles according to three different dimensions: (i) intensity of user activities, (ii) tag-based characteristics of user profiles, and (iii) semantic characteristics of user profiles.

References

[1]

Abel, F., Herder, E., Houben, G.-J., Henze, N., and Krause, D. 2011. Cross-system user modeling and personalization on the social web. User Model. User-Adapted Interact. 1--42.

[2]

Adomavicius, G., Sankaranarayanan, R., Sen, S., and Tuzhilin, A. 2005. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. 23, 1, 103--145.

Digital Library

[3]

Angeletou, S., Sabou, M., and Motta, E. 2009. Improving folksonomies using formal knowledge: A case study on search. Semantic Web, 276--290.

Digital Library

[4]

Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., and Ives, Z. 2007. DBpedia: A nucleus for a web of open data. In Proceedings of the 6th International Semantic Web Conference. 715--728.

Digital Library

[5]

Begelman, G., Keller, P., and Smadja, F. 2006. Automated tag clustering: improving search and exploration in the tag space. In Proceedings of the Collaborative Web Tagging Workshop. http://imreal-project.eu.

[6]

Cantador, I., Konstas, I., and Jose, J. 2011. Categorising social tags to improve folksonomy-based recommendations. Web Semantics: Science, Services Agents WWW 9, 1, 1--15.

Digital Library

[7]

Carmagnola, F. and Cena, F. 2009. User identification for cross-system personalisation. Inf. Sci. Int. J. 179, 1--2, 16--32.

Digital Library

[8]

Catanese, S., De Meo, P., Ferrara, E., Fiumara, G., and Provetti, A. 2011. Crawling facebook for social network analysis purposes. In Proceedings of the International Conference On Web Intelligence, Mining And Semantics. 52:1--52:8.

Digital Library

[9]

De Gemmis, M., Lops, P., Semeraro, G., and Basile, P. 2008. Integrating tags in a semantic content-based recommender. In Proceedings of the ACM Conference on Recommender Systems (RecSys'08). ACM, 163--170.

Digital Library

[10]

De Meo, P., Ferrara, E., Fiumara, G., and Provetti, A. 2011. Improving recommendation quality by merging collaborative filtering and social relationships. In Proceedings of the 11th International Conference on Intelligent Systems Design And Applications. IEEE, 587--592.

[11]

De Meo, P., Nocera, A., Quattrone, G., Rosaci, D., and Ursino, D. 2009a. Finding reliable users and social networks in a social internetworking system. In Proceedings of the International Database Engineering and Applications Symposium (IDEAS'09). 173--181.

Digital Library

[12]

De Meo, P., Quattrone, G., and Ursino, D. 2009b. Exploitation of semantic relationships and hierarchical data structures to support a user in his annotation and browsing activities in folksonomies. Inf. Syst. 34, 6, 511--535.

Digital Library

[13]

De Meo, P., Quattrone, G., and Ursino, D. 2010. A query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy. User Model. User-Adapt. Interact. 20, 1, 41--86.

Digital Library

[14]

Dunbar, R. 1998. Grooming, Gossip, and the Evolution of Language. Harvard Universing Press.

[15]

Fellbaum, C., Ed. 1998. WordNet: An Electronic Lexical Database. MIT Press.

[16]

Ferrara, E. 2012a. Community structure discovery in facebook. Int. J. Social Network Mining 1, 1, 67--90.

[17]

Ferrara, E. 2012b. A large-scale community structure analysis in facebook. EPJ Data Sci. 1, 1, 9.

[18]

Gilbert, E. and Karahalios, K. 2009. Predicting tie strength with social media. In Proceedings of the 27th International Conference on Human Factors in Computing Systems (CHI'09). ACM, 211--220.

Digital Library

[19]

Hotho, A., Jäschke, R., Schmitz, C., and Stumme, G. 2006. Information retrieval in folksonomies: Search and ranking. In Proceedings of the 3rd European Semantic Web Conference. Lecture Notes in Computer Science, vol. 4011, Springer, 411--426.

Digital Library

[20]

Iofciu, T., Fankhauser, P., Abel, F., and Bischoff, K. 2010. Identifying users across social tagging systems. Tech. rep., L3S Research Center.

[21]

Iofciu, T., Fankhauser, P., Abel, F., and Bischoff, K. 2011. Identifying users across social tagging systems. In Proceedings of the International Conference on Weblogs and Social Media (ICWSM'11). AAAI Press.

[22]

Mendes, P. N., Jakob, M., García-Silva, A., and Bizer, C. 2011. Dbpedia spotlight: Shedding light on the web of documents. In Proceedings of the 7th International Conference on Semantic Systems.

Digital Library

[23]

Mika, P. 2005. Ontologies Are Us: A unified model of social networks and semantics. In Proceedings of the International Semantic Web Conference (ISWC'05). 522--536.

Digital Library

[24]

Pan, J., Taylor, S., and Thomas, E. 2009. Reducing ambiguity in tagging systems with folksonomy search expansion. Semantic Web: Res. Appl. 669--683.

Digital Library

[25]

Schmitz, P. 2006. Inducing ontology from Flickr tags. In Proceedings of the Collaborative Web Tagging Workshop.

[26]

Shannon, C. 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27.

[27]

Shi, Y., Larson, M., and Hanjalic, A. 2011. Tags as bridges between domains: Improving recommendation with tag-induced cross-domain collaborative filtering. In Proceedings of the 19th International Conference on User Modeling, Adaption and Personalization, J. A. Konstan, R. Conejo, J. L. Marzo, and N. Oliver, Eds., Lecture Notes in Computer Science, vol. 6787, Springer, 305--316.

Digital Library

[28]

Stewart, A., Diaz-Aviles, E., Nejdl, W., Marinho, L. B., Nanopoulos, A., and Schmidt-Thieme, L. 2009. Cross-tagging for personalized open social networking. In Proceedings of the 20th ACM Conference on Hypertext and Hypermedia (Hypertext'09). C. Cattuto, G. Ruffo, and F. Menczer, Eds., ACM, 271--278.

Digital Library

[29]

Szomszor, M., Cantador, I., and Alani, H. 2008. Correlating user profiles from multiple folksonomies. In Proceedings of the 19th ACM Conference on Hypertext and Hypermedia (HT'08). ACM, 33--42.

Digital Library

[30]

Tso-Sutter, K., Marinho, L., and Schmidt-Thieme, L. 2008. Tag-aware recommender systems by fusion of collaborative filtering algorithms. In Proceedings of the ACM Symposium on Applied Computing (SAC'08). ACM Press, 1995--1999.

Digital Library

[31]

Vander Wal, T. 2005. Explaining and showing broad and narrow folksonomies. http://www.personalinfocloud.com/2005/02/explaining.and.html.

[32]

Vasuki, V., Natarajan, N., Lu, Z., Savas, B., and Dhillon, I. 2011. Scalable affiliation recommendation using auxiliary networks. ACM Trans. Intell. Syst. Technol. 3, 1, 3.

Digital Library

[33]

Vosecky, J., Hong, D., and Shen, V. Y. 2009. User identification across multiple social networks. In Proceedings of the 1st International Conference on Networked Digital Technologies (NDT'09). IEEE, 360--365.

[34]

Wu, X., Zhang, L., and Yu, Y. 2006. Exploring social annotations for the Semantic Web. In Proceedings of the International Conference on World Wide Web (WWW'06). ACM, 417--426.

Digital Library

[35]

Yeung, C., Gibbins, N., and Shadbolt, N. 2007. Tag meaning disambiguation through analysis of tripartite structure of folksonomies. In Proceedings of the International Conference on Web Intelligence and International Conference on Intelligent Agent Technology Workshops (WI-IAT'07 Workshops). IEEE, 3--6.

Digital Library

[36]

Yuan, Q., Chen, L., and Zhao, S. 2011. Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation. In Proceedings of the ACM Conference on Recommender Systems (RecSys'11). ACM, 245--252.

Digital Library

[37]

Zafarani, R. and Liu, H. 2009. Connecting corresponding identities across communities. In Proceedings of the International Conference on Weblogs and Social Media. 354--357.

Cited By

Xie XZang WHu YJi JXiong Z(2024)Novel Method of Edge-Removing Walk for Graph Representation in User Identity LinkageElectronics10.3390/electronics1304071513:4(715)Online publication date: 9-Feb-2024
https://doi.org/10.3390/electronics13040715
Taha K(2023)Context-Driven-Based Community DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.319343910:2(623-639)Online publication date: Apr-2023
https://doi.org/10.1109/TCSS.2022.3193439
Meriem HAbdelwahed EQassimi S(2022)Time-Aware User Profile Enrichment in the Collective Intelligence ContextAdvanced Intelligent Systems for Sustainable Development (AI2SD’2020)10.1007/978-3-030-90639-9_22(277-290)Online publication date: 10-Feb-2022
https://doi.org/10.1007/978-3-030-90639-9_22
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Index Terms

Analyzing user behavior across social sharing environments
1. Information systems
  1. Information retrieval
    1. Document representation
    2. Users and interactive retrieval
      1. Personalization
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 5, Issue 1

Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining

December 2013

520 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2542182

Editors:
Dr. Hui Xiong
Rutgers University http://datamining.rutgers.edu/
,
Dr. Shashi Shekhar
University of Minnesota http://www-users.cs.umn.edu/∼shekhar/
,
Dr. Alexander Tuzhilin
New York University http://pages.stern.nyu.edu/∼atuzhili/
,
Francesco Bonchi
Yahoo! Research Barcelona, Spain
,
Wray Buntine
NICTA, Australia and The Australian National University
,
Ricard Gavaldá
Technical University of Catalonia, Spain
,
Shengbo Guo
Xerox Research Centre Europe, France

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Copyright © 2014 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 January 2014

Accepted: 01 March 2013

Revised: 01 June 2012

Received: 01 March 2012

Published in TIST Volume 5, Issue 1

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https://doi.org/10.3390/electronics13040715
Taha K(2023)Context-Driven-Based Community DetectionIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.319343910:2(623-639)Online publication date: Apr-2023
https://doi.org/10.1109/TCSS.2022.3193439
Meriem HAbdelwahed EQassimi S(2022)Time-Aware User Profile Enrichment in the Collective Intelligence ContextAdvanced Intelligent Systems for Sustainable Development (AI2SD’2020)10.1007/978-3-030-90639-9_22(277-290)Online publication date: 10-Feb-2022
https://doi.org/10.1007/978-3-030-90639-9_22
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