Understanding Privacy Dichotomy in Twitter

Balancing personalization and privacy is one of the challenges marketers commonly face. The privacy dilemmas associated with personalized services are particularly concerning in the context of social networking websites, wherein the privacy dichotomy problem is widely observed. To prevent potential privacy violations, businesses need to employ multiple safeguards beyond the current privacy settings of users. As a possible solution, companies can utilize user social footprints to detect user privacy preferences. To take a step towards this goal, we first ran a series of experiments to examine if the privacy preference attribute is homophilous in social media. As a result, we found a set of clues that users' privacy preferences are similar to the privacy behaviour of their social contacts, signaling that privacy homophily exists in social networks. We further studied users located in different neighbourhoods with varying degrees of privacy and found a set of characteristics that are specific to public users located in private neighbourhoods. These identified features can be used in a predictive model to identify public user accounts that are intended to be private, supporting companies to make an informed decision whether or not to exploit one's publicly available data for personalization purposes.

[1]  Eytan Adar,et al.  The PViz comprehension tool for social network privacy settings , 2012, SOUPS.

[2]  Kris Vanhecke,et al.  Privacy Aspects of Recommender Systems , 2015, Recommender Systems Handbook.

[3]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[4]  Kristen LeFevre,et al.  Privacy wizards for social networking sites , 2010, WWW '10.

[5]  George Danezis Inferring privacy policies for social networking services , 2009, AISec '09.

[6]  Jürgen Pfeffer,et al.  Identifying Platform Effects in Social Media Data , 2016, ICWSM.

[7]  Heather Richter Lipford,et al.  Understanding Privacy Settings in Facebook with an Audience View , 2008, UPSEC.

[8]  Anna Cinzia Squicciarini,et al.  User Centric Policy Management in Online Social Networks , 2010, 2010 IEEE International Symposium on Policies for Distributed Systems and Networks.

[9]  Robert E. Mercer,et al.  Detecting Privacy Preferences from Online Social Footprints: A Literature Review , 2016, iConference 2016 Proceedings.

[10]  Krishna P. Gummadi,et al.  Analyzing facebook privacy settings: user expectations vs. reality , 2011, IMC '11.

[11]  Bart P. Knijnenburg,et al.  Profiling Facebook Users' Privacy Behaviors , 2014 .

[12]  Dan Lin,et al.  Privacy Policy Inference of User-Uploaded Images on Content Sharing Sites , 2015, IEEE Transactions on Knowledge and Data Engineering.

[13]  Alfred Kobsa,et al.  Privacy-enhanced personalization , 2006, FLAIRS.

[14]  Dan Lin,et al.  Identifying hidden social circles for advanced privacy configuration , 2014, Comput. Secur..

[15]  Mohamed Shehab,et al.  Semi-Supervised Policy Recommendation for Online Social Networks , 2012, ASONAM.

[16]  Robert E. Mercer,et al.  Privacy Behaviour and Profile Configuration in Twitter , 2016, WWW.

[17]  Claudia Niederée,et al.  Analyzing and Predicting Privacy Settings in the Social Web , 2015, UMAP.

[18]  Hongxia Jin,et al.  Predicting Privacy Behavior on Online Social Networks , 2015, ICWSM.

[19]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[20]  Rachel Greenstadt,et al.  Privacy Detective: Detecting Private Information and Collective Privacy Behavior in a Large Social Network , 2014, WPES.

[21]  Patrick P. Tsang,et al.  Social Circles: Tackling Privacy in Social Networks , 2008 .

[22]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Yang Wang,et al.  Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems , 2012, User Modeling and User-Adapted Interaction.

[24]  Wendy Liu,et al.  Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors , 2012, ICWSM.

[25]  Jimmy J. Lin,et al.  Information network or social network?: the structure of the twitter follow graph , 2014, WWW.

[26]  Dan Lin,et al.  Automatic social group organization and privacy management , 2012, 8th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom).

[27]  Bettina Berendt,et al.  Circles, posts and privacy in egocentric social networks: An exploratory visualization approach , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[28]  Heather Richter Lipford,et al.  Strategies and struggles with privacy in an online social networking community , 2008, BCS HCI.

[29]  Asimina Vasalou,et al.  Privacy dictionary: a linguistic taxonomy of privacy for content analysis , 2011, CHI.

[30]  G. Geethakumari,et al.  Measuring privacy leaks in Online Social Networks , 2013, 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

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

[32]  Alessandro Acquisti,et al.  Imagined Communities: Awareness, Information Sharing, and Privacy on the Facebook , 2006, Privacy Enhancing Technologies.

[33]  J. Pennebaker,et al.  Psychological aspects of natural language. use: our words, our selves. , 2003, Annual review of psychology.

[34]  Evimaria Terzi,et al.  A Framework for Computing the Privacy Scores of Users in Online Social Networks , 2009, ICDM.

[35]  Eduard H. Hovy,et al.  Weakly Supervised User Profile Extraction from Twitter , 2014, ACL.