Why Checkins: Exploring User Motivation on Location Based Social Networks

Checkins, the niche service provided by location based social networks (LBSN), bridge users' online activities and offline social lives in a seamless way. Therefore, knowledge discovery on check in data has become an important research direction [1], [2], [3], [4]. However, a fundamental and interesting question about checkins remains unanswered yet. What are people's motivations behind those checkins? We give the first attempt to answer this question. Motivation studies first appear in social psychology in a less quantitative way. For example, the goal-directed behavior (MGB) model [5] uncovers the association between behaviors and motivations. Following a similar rationale, we design a computational model for the mining of user check in motivations from large scale real world data. We assume that the check in motivation has two types: social motivation and individual motivation. Social motivation is the type of check in incentive that stimulates interactions or influences among friends. Individual motivation is another type of check in incentive that aims to explore and share attractive places. Following the structure of the MGB model, we construct user check in motivation prediction model (UCMP) and then formalize the motivation prediction problem as an optimization problem. The idea is minimizing the difference between the estimated behavior and the true behavior to get the predicted motivations. The experiment on this GOWALLA dataset shows not only prediction results, but also very interesting phenomenons about social users and social locations.