Analysis of a Heterogeneous Social Network of Humans and Cultural Objects

Modern online social platforms allow their members to be involved in a broad range of activities including getting friends, joining groups, posting, and commenting resources. In this paper, we investigate whether a correlation emerges across the different activities a user can take part in. For our analysis, we focused on aNobii, a social platform with a world-wide user base of book readers, who post their readings, give ratings, review books, and discuss them with friends and fellow readers. aNobii presents a heterogeneous structure: 1) part social network, with user-to-user interactions; 2) part interest network, with the management of book collections; and 3) part folksonomy, with books that are tagged by the users. We analyzed a complete snapshot of aNobii and we focused on three specific activities a user can perform, namely tagging behavior, tendency to join groups and aptitude to compile a wishlist of the books one is planning to read. For each user, we create a tag-based, a group-based, and a wishlist-based profile. Experimental analysis, which was carried out with information-theory tools like entropy and mutual information, suggests that tag-based and group-based profiles are in general more informative than wishlist-based ones. Furthermore, we discover that the degree of correlation between the three profiles associated with the same user tend to be small. Hence, user profiling cannot be reduced to considering just any one type of user activity (albeit important) but it is crucial to incorporate multiple dimensions to effectively describe users' preferences and behavior.

[1]  Chao Liu,et al.  Recommender systems with social regularization , 2011, WSDM '11.

[2]  Esteban Moro,et al.  Social Features of Online Networks: The Strength of Intermediary Ties in Online Social Media , 2011, PloS one.

[3]  Lora Aroyo,et al.  Analyzing user behavior across social sharing environments , 2013, ACM Trans. Intell. Syst. Technol..

[4]  Rossano Schifanella,et al.  Folks in Folksonomies: social link prediction from shared metadata , 2010, WSDM '10.

[5]  Emilio Ferrara,et al.  Topological Features of Online Social Networks , 2011, ArXiv.

[6]  Huan Liu,et al.  Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose , 2013, ICWSM.

[7]  Filippo Menczer,et al.  The Digital Evolution of Occupy Wall Street , 2013, PloS one.

[8]  A. Vespignani,et al.  Competition among memes in a world with limited attention , 2012, Scientific Reports.

[9]  Scott A. Golder,et al.  Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures , 2011 .

[10]  Ruixuan Li,et al.  Topic-based ranking in Folksonomy via probabilistic model , 2011, Artificial Intelligence Review.

[11]  Nazareno Andrade,et al.  Individual and social behavior in tagging systems , 2009, HT '09.

[12]  Damon Centola,et al.  The Spread of Behavior in an Online Social Network Experiment , 2010, Science.

[13]  Johan Bollen,et al.  Happiness Is Assortative in Online Social Networks , 2011, Artificial Life.

[14]  Edward Y. Chang,et al.  Combinational collaborative filtering for personalized community recommendation , 2008, KDD.

[15]  Cosma Rohilla Shalizi,et al.  Homophily and Contagion Are Generically Confounded in Observational Social Network Studies , 2010, Sociological methods & research.

[16]  D. W. Scott,et al.  Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .

[17]  Edward Y. Chang,et al.  Collaborative filtering for orkut communities: discovery of user latent behavior , 2009, WWW '09.

[18]  Giovanni Quattrone,et al.  A query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy , 2010, User Modeling and User-Adapted Interaction.

[19]  Jie Tang,et al.  Inferring social ties across heterogenous networks , 2012, WSDM '12.

[20]  Mark S Handcock,et al.  MODELING SOCIAL NETWORKS FROM SAMPLED DATA. , 2010, The annals of applied statistics.

[21]  P. Good.  Permutation, Parametric, and Bootstrap Tests of Hypotheses , 2005 .

[22]  Rossano Schifanella,et al.  Friendship prediction and homophily in social media , 2012, TWEB.

[23]  Mathias Lux,et al.  Aspects of Broad Folksonomies , 2007, 18th International Workshop on Database and Expert Systems Applications (DEXA 2007).

[24]  Lise Getoor,et al.  Relationship Identification for Social Network Discovery , 2007, AAAI.

[25]  Minas Gjoka,et al.  Practical Recommendations on Crawling Online Social Networks , 2011, IEEE Journal on Selected Areas in Communications.

[26]  Stefan Siersdorfer,et al.  Social recommender systems for web 2.0 folksonomies , 2009, HT '09.

[27]  Salvatore Catanese,et al.  Crawling Facebook for social network analysis purposes , 2011, WIMS '11.

[28]  Francis R. Bach,et al.  Online Learning for Latent Dirichlet Allocation , 2010, NIPS.

[29]  Jure Leskovec,et al.  No country for old members: user lifecycle and linguistic change in online communities , 2013, WWW.

[30]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[31]  Behzad Moshiri,et al.  Fusing Data and Optimizing Queries for Intelligent Search , 2007 .

[32]  Anna Monreale,et al.  Multidimensional networks: foundations of structural analysis , 2013, World Wide Web.

[33]  Katarzyna Musial,et al.  Multi-Layered Social Network Creation Based on Bibliographic Data , 2010, 2010 IEEE Second International Conference on Social Computing.

[34]  Antonino Nocera,et al.  Recommendation of similar users, resources and social networks in a Social Internetworking Scenario , 2011, Inf. Sci..

[35]  Katarzyna Musial,et al.  Multidimensional Social Network in the Social Recommender System , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[36]  Kristina Lerman,et al.  Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks , 2010, ICWSM.

[37]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[38]  Rajat Raina,et al.  Learning relevance from heterogeneous social network and its application in online targeting , 2011, SIGIR.

[39]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[40]  Rossano Schifanella,et al.  Link Creation and Profile Alignment in the aNobii Social Network , 2010, 2010 IEEE Second International Conference on Social Computing.

[41]  Filippo Menczer,et al.  The Geospatial Characteristics of a Social Movement Communication Network , 2013, PloS one.

[42]  Yi-Cheng Zhang,et al.  Solving the apparent diversity-accuracy dilemma of recommender systems , 2008, Proceedings of the National Academy of Sciences.

[43]  Valentin Robu,et al.  The complex dynamics of collaborative tagging , 2007, WWW '07.

[44]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[45]  Rossano Schifanella,et al.  Link creation and information spreading over social and communication ties in an interest-based online social network , 2012, EPJ Data Science.

[46]  Emilio Ferrara,et al.  A large-scale community structure analysis in Facebook , 2011, EPJ Data Science.

[47]  Jiawei Han,et al.  Mining advisor-advisee relationships from research publication networks , 2010, KDD.

[48]  Jon M. Kleinberg,et al.  Group formation in large social networks: membership, growth, and evolution , 2006, KDD '06.

[49]  Vittorio Loreto,et al.  Network properties of folksonomies , 2007, AI Commun..

[50]  Wei Zeng,et al.  A unified framework for recommending items, groups and friends in social media environment via mutual resource fusion , 2013, Expert Syst. Appl..

[51]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[52]  Georgia Koutrika,et al.  Can social bookmarking improve web search? , 2008, WSDM '08.

[53]  Rossano Schifanella,et al.  People Are Strange When You're a Stranger: Impact and Influence of Bots on Social Networks , 2012, ICWSM.