A person's beliefs and attitudes may change multiple times as they gain additional information/perceptions from various external sources, which in turn, may affect their subsequent behavior. Such influential sources, however, are often invisible to the public due to a variety of reasons - private communications, what one randomly reads or hears, and implicit social hierarchies, to name a few. Many efforts have focused on detecting distribution variations. However, the underlying reason for the variation has yet to be fully studied. In this paper, we present a novel approach and algorithm to detect such hidden sources, as well as capture and characterize the patterns of their impact with regards to the belief-changing trend. We formalize this problem as a finite belief fusion model and solve it via an optimization method. Finally, we compare our work with general mixture models, e.g. Gaussian Mixture Model. We present promising preliminary results obtained from proof-of-concept experiments conducted on both synthetic data and a real-world scenario.