Following the advent of location-based social networks (LBSNs), location-aware services have attracted considerable attention among researchers. Research has shown that the social network is regarded as one of the strongest influences shaping individual attitudes and behaviors. This paper targets the mining of location-based social influences hidden in LBSNs. In other words, we sought to determine whether an individual’s check-in behavior is influenced by friends’ check-ins. Check-in data includes positional information; therefore, we refer to this type of influence as spatiotemporal social influences. This study proposes a framework for spatiotemporal social influence mining (ST-SIM) to identify users with the greatest influence on individuals (i.e., close friends and travel experts) from an LBSN and estimate the strength of these social connections. Explicitly, the proposed framework is able to infer a list of influential users of an individual under given conditions based on travel distance, visiting time or POI categories. We developed a diffusion-based mechanism for modeling the propagation of influence over time. Our experiment results demonstrate that the ST-SIM framework outperforms state-of-the-art methods in terms of accuracy and reliability, and is applicable in domains ranging from marketing to intelligence analysis.