Spatial influence - measuring followship in the real world

Abstract

Finding influential people in a society has been the focus of social studies for decades due to its numerous applications, such as viral marketing or spreading ideas and practices. A critical first step is to quantify the amount of influence an individual exerts on another, termed pairwise influence. Early social studies had to confine themselves to surveys and manual data collections for this purpose; more recent studies have exploited web data (e.g., blogs). In this paper, for the first time, we utilize people's movement in the real world (aka spatiotemporal data) to derive pairwise influence. We first define followship to capture the phenomenon of an individual visiting a real-world location (e.g., restaurant) due the influence of another individual who has visited that same location in the past. Subsequently, we coin the term spatial influence as the concept of inferring pairwise influence from spatiotemporal data by quantifying the amount of followship influence that an individual has on others. We then propose the Temporal and Locational Followship Model (TLFM) to estimate spatial influence, in which we study three factors that impact followship: the time delay between the visits, the popularity of the location, and the inherent coincidences in individuals' visiting behaviors. We conducted extensive experiments using various real-world datasets, which demonstrate the effectiveness of our TLFM model in quantifying spatial influence.