Differentiated Incentive Rewarding for Social Networking Services

Our ultimate goal is to develop an information diffusion system where individuals are motivated to create content and share it with public. As our first step, in this paper, we present an incentive-rewarding mechanism for social networking services and focus particularly on changing reward assignment ratio considering different risks users perceive when uploading content with different privacy settings: public-open and friend-limited. Our learning-based simulation allowed us to observe enlargement of social networks with different rewarding ratio. The result also suggested that there is an optimal reward assignment ratio to maximize social networks.