We have developed an incentive-rewarding mechanism that stimulates activities in social networking services (SNSs), including content uploading and link establishment. We particularly focus on changing the reward assignment ratio based on the different risks users perceive when uploading content with different privacy settings: public-open and friend-limited. Learning-based simulation allowed us to observe that SNS activity, which we measured as the amount of browsed content within a certain period, can be controlled by a rewarding assignment ratio. We then analyzed how the amount of uploaded content and the increase of established links affect SNS activity. Results suggested that the optimal reward assignment ratio to maximize SNS activity changes depending on the total amount of available reward resources.
ASJC Scopus subject areas
- Computer Science Applications
- Computer Networks and Communications