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Sensor placement for the purpose of detecting/tracking news outbreak andpreventing rumor spreading is a challenging problem in a large scale onlinesocial network (OSN). This problem is a kind of subset selection problem:choosing a small set of items from a large population so to maximize someprespecified set function. However, it is known to be NP-complete. Existingheuristics are very costly especially for modern OSNs which usually containhundreds of millions of users. This paper aims to design methods to find\emph{good solutions} that can well trade off efficiency and accuracy. We firstshow that it is possible to obtain a high quality solution with a probabilisticguarantee from a "{\em candidate set}" of the underlying social network. Byexploring this candidate set, one can increase the efficiency of placing socialsensors. We also present how this candidate set can be obtained using "{\emgraph sampling}", which has an advantage over previous methods of not requiringthe prior knowledge of the complete network topology. Experiments carried outon two real datasets demonstrate not only the accuracy and efficiency of ourapproach, but also effectiveness in detecting and predicting news outbreak.

Social Sensor Placement in Large Scale Networks: A Graph Sampling Perspective