While humans can act as effective sensors, human input is subject to a high degree of error and highly dependent on the context. Furthermore, extracting the signal from the noise for social sensing is a difficult challenge. One approach to improving the accuracy of social sensing is to use physical sensors as a control knob for social sensing algorithms. In this paper, we present an architecture for using audio sensors as a way to control an algorithm used for social sensing of interesting events. We present various use cases where the architecture is applicable, and go into the details of one specific use case, namely using crowd behavior in a golf-course to identify and control social media feeds related to the course.