Critical Source Selection in Social Sensing Applications

Social sensing has emerged as a new data collection paradigm in networked sensing applications where humans are used as "sensors" to report their observations about the physical world. While many previous studies in social sensing focus on the problem of ascertaining the reliability of data sources and the correctness of their reported claims (often known as truth discovery), this paper investigates a new problem of critical source selection. The goal of this problem is to identify a subset of critical sources that can help effectively reduce the computational complexity of the original truth discovery problem and improve the accuracy of the analysis results. In this paper, we propose a new scheme, Critical Sources Selection (CSS) scheme, to find the critical set of sources by explicitly exploring both dependency and speak rate of sources. We evaluated the performance of our scheme and compared it to the state-of-the-art baselines using two data traces collected from a real world social sensing application. The results showed that our scheme significantly outperforms the baselines by finding more truthful information at a faster speed.