Revealing censored information through comments and commenters in online social networks

In this work we study information leakage through discussions in online social networks. In particular, we focus on articles published by news pages, in which a person's name is censored, and we examine whether the person is identifiable (de-censored) by analyzing comments and social network graphs of commenters. As a case study for our proposed methodology, in this paper we considered 48 articles (Israeli, military related) with censored content, followed by a threaded discussion. We qualitatively study the set of comments and identify comments (in this case referred as "leakers") and the commenter and the censored person. We denote these commenters as "leakers". We found that such comments are present for some 75% of the articles we considered. Finally, leveraging the social network graphs of the leakers, and specifically the overlap among the graphs of the leakers, we are able to identify the censored person. We show the viability of our methodology through some illustrative use cases.

[1]  Jacob Goldenberg,et al.  Mine Your Own Business: Market-Structure Surveillance Through Text Mining , 2012, Mark. Sci..

[2]  Mauro Conti,et al.  Friend in the Middle (FiM): Tackling de-anonymization in social networks , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[3]  Allan R. Wilks,et al.  Fraud Detection in Telecommunications: History and Lessons Learned , 2010, Technometrics.

[4]  V. Ferreira,et al.  Don't Talk About Pink Elephants! , 2006, Psychological science.

[5]  Mauro Conti,et al.  VirtualFriendship: Hiding interactions on Online Social Networks , 2014, 2014 IEEE Conference on Communications and Network Security.

[6]  Joel M. Schumm.  No Names, Please: The Virtual Victimization of Children, Crime Victims, the Mentally Ill, and Others in Appellate Court Opinions , 2007 .

[7]  K. Selçuk Candan,et al.  How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media? , 2010, ICWSM.

[8]  Ranjeev Mittu,et al.  Human Factors in Cybersecurity and the Role for AI , 2015, AAAI Spring Symposia.

[9]  Panagiotis G. Ipeirotis,et al.  Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics , 2010, IEEE Transactions on Knowledge and Data Engineering.

[10]  Ari Rappoport,et al.  ICWSM - A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews , 2010, ICWSM.

[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]  David G. Schwartz,et al.  News censorship in online social networks: A study of circumvention in the commentsphere , 2017, J. Assoc. Inf. Sci. Technol..

[13]  Alessandro Acquisti,et al.  Information revelation and privacy in online social networks , 2005, WPES '05.

[14]  Beibei Li,et al.  Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowd-Sourced Content , 2011, Mark. Sci..

[15]  Mark Dredze,et al.  You Are What You Tweet: Analyzing Twitter for Public Health , 2011, ICWSM.

[16]  Mohd Anwar,et al.  Mutual-friend based attacks in social network systems , 2013, Comput. Secur..

[17]  David G. Schwartz,et al.  Detecting unintentional information leakage in social media news comments , 2014, Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014).

[18]  Mauro Conti,et al.  SocialSpy: Browsing (Supposedly) Hidden Information in Online Social Networks , 2014, CRiSIS.

[19]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[20]  Diederik W. van Liere,et al.  How far does a tweet travel?: Information brokers in the twitterverse , 2010, MSM '10.

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

[22]  Michelle Johnson.  Of public interest: How courts handle rape victims' privacy suits , 1999 .

[23]  Scott Counts,et al.  Predicting the Speed, Scale, and Range of Information Diffusion in Twitter , 2010, ICWSM.

[24]  Lior Rokach,et al.  A Survey of Data Leakage Detection and Prevention Solutions , 2012, SpringerBriefs in Computer Science.

[25]  Rodger A. Bates,et al.  Psychological Operations and Terrorism: The Digital Domain , 2014 .

[26]  Jian Pei,et al.  Preserving Privacy in Social Networks Against Neighborhood Attacks , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[27]  Michael J. Liersch,et al.  Can you keep a secret? Increasing speakers’ motivation to keep information confidential yields poorer outcomes , 2012 .