  The number of missing people (i.e., people who get lost) greatly increases in
recent years. It is a serious worldwide problem, and finding the missing people
consumes a large amount of social resources. In tracking and finding these
missing people, timely data gathering and analysis actually play an important
role. With the development of social media, information about missing people
can get propagated through the web very quickly, which provides a promising way
to solve the problem. The information in online social media is usually of
heterogeneous categories, involving both complex social interactions and
textual data of diverse structures. Effective fusion of these different types
of information for addressing the missing people identification problem can be
a great challenge. Motivated by the multi-instance learning problem and
existing social science theory of "homophily", in this paper, we propose a
novel r-instance (RI) learning model.
