Am i more similar to my followers or followees?: analyzing homophily effect in directed social networks

Homophily is the formation of social ties between two individuals due to similar characteristics or interests. Based on homophily, in a social network it is expected to observe a higher degree of homogeneity among connected than disconnected people. Many researchers use this simple yet effective principal to infer users' missing information and interests based on the information provided by their neighbors. In a directed social network, the neighbors can be further divided into followers and followees. In this work, we investigate the homophily effect in a directed network. To explore the homophily effect in a directed network, we study if a user's personal preferences can be inferred from those of users connected to her (followers or followees). We investigate which of followers or followees are more effective in helping to infer users' personal preferences. Our findings can help to raise the awareness of users over their privacy and can help them better manage their privacy.

[1]  Mohammad Ali Abbasi,et al.  Real-World Behavior Analysis through a Social Media Lens , 2012, SBP.

[2]  Daniele Quercia,et al.  The personality of popular facebook users , 2012, CSCW.

[3]  Rui Wang,et al.  Towards social user profiling: unified and discriminative influence model for inferring home locations , 2012, KDD.

[4]  Jacob Ratkiewicz,et al.  Predicting the Political Alignment of Twitter Users , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[5]  Reza Zafarani,et al.  Social Media Mining: An Introduction , 2014 .

[6]  Krishna P. Gummadi,et al.  You are who you know: inferring user profiles in online social networks , 2010, WSDM '10.

[7]  S. Gosling,et al.  A room with a cue: personality judgments based on offices and bedrooms. , 2002, Journal of personality and social psychology.

[8]  Long Jiang,et al.  User-level sentiment analysis incorporating social networks , 2011, KDD.

[9]  Mohammad Ali Abbasi,et al.  Measuring User Credibility in Social Media , 2013, SBP.

[10]  Daniele Quercia,et al.  Our Twitter Profiles, Our Selves: Predicting Personality with Twitter , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[11]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[12]  Bernd Marcus,et al.  Personality in cyberspace: personal Web sites as media for personality expressions and impressions. , 2006, Journal of personality and social psychology.

[13]  Behram F. T. Mistree,et al.  Gaydar: Facebook Friendships Expose Sexual Orientation , 2009, First Monday.

[14]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[15]  Hua Li,et al.  Demographic prediction based on user's browsing behavior , 2007, WWW '07.

[16]  Mohamed Ali Kâafar,et al.  You are what you like! Information leakage through users' Interests , 2012, NDSS.

[17]  Dan Murray,et al.  Inferring Demographic Attributes of Anonymus Internet Users , 1999, WEBKDD.

[18]  D. Rao.  Detecting Latent User Properties in Social Media , 2010 .

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