TY - GEN
T1 - Intruder or welcome friend
T2 - 6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013
AU - Lesser, Ofrit
AU - Tenenboim-Chekina, Lena
AU - Rokach, Lior
AU - Elovici, Yuval
PY - 2013/3/14
Y1 - 2013/3/14
N2 - Inferring Online Social Networks (OSN) group members may help to evaluate the authenticity of an applicant asking to join a certain group, and secure vulnerable populations online, such as children. We propose machine learning based methods, which associate OSN members' affiliation with virtual groups based on personal, topological, and group affiliation features. The study applies and evaluates the methods empirically, on two social networks (Ning and TheMarker). The experimental results demonstrate that one can accurately determine the group genuine members. Our study compares personal, topological and group based classification models. The results show that topological and group affiliation attributes contribute the most to group inference accuracy. Additionally, we examine the relations among the groups and identify group clustering tendencies where some groups are more tightly connected than others.
AB - Inferring Online Social Networks (OSN) group members may help to evaluate the authenticity of an applicant asking to join a certain group, and secure vulnerable populations online, such as children. We propose machine learning based methods, which associate OSN members' affiliation with virtual groups based on personal, topological, and group affiliation features. The study applies and evaluates the methods empirically, on two social networks (Ning and TheMarker). The experimental results demonstrate that one can accurately determine the group genuine members. Our study compares personal, topological and group based classification models. The results show that topological and group affiliation attributes contribute the most to group inference accuracy. Additionally, we examine the relations among the groups and identify group clustering tendencies where some groups are more tightly connected than others.
KW - group prediction
KW - machine learning
KW - social networks
UR - http://www.scopus.com/inward/record.url?scp=84874813921&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37210-0_40
DO - 10.1007/978-3-642-37210-0_40
M3 - Conference contribution
AN - SCOPUS:84874813921
SN - 9783642372094
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 368
EP - 376
BT - Social Computing, Behavioral-Cultural Modeling and Prediction - 6th International Conference, SBP 2013, Proceedings
Y2 - 2 April 2013 through 5 April 2013
ER -