Intruder or welcome friend: inferring group membership in online social networks

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.