TY - GEN
T1 - Revealing censored information through comments and commenters in online social networks
AU - Cascavilla, Giuseppe
AU - Conti, Mauro
AU - Schwartz, David G.
AU - Yahav, Inbal
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/8/25
Y1 - 2015/8/25
N2 - 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 (decensored) 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.
AB - 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 (decensored) 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.
UR - https://www.scopus.com/pages/publications/84962571907
U2 - 10.1145/2808797.2809290
DO - 10.1145/2808797.2809290
M3 - Conference contribution
AN - SCOPUS:84962571907
T3 - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
SP - 675
EP - 680
BT - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
A2 - Pei, Jian
A2 - Tang, Jie
A2 - Silvestri, Fabrizio
PB - Association for Computing Machinery
T2 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Y2 - 25 August 2015 through 28 August 2015
ER -