Taku Konishi

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.

Content Propagation Analysis of E-mail Communications

We analyze email communications within a large company to reveal how email activity patterns depend on content. We characterize email contents using keywords and examine statistics of email transmissions. As a result, we are able to identify differences in network structures and propagation behaviors depending on the type of keyword.We analyze email communications within a large company to reveal how email activity patterns depend on content. We characterize email contents using keywords and examine statistics of email transmissions. As a result, we are able to identify differences in network structures and propagation behaviors depending on the type of keyword.

Experiments on Personal Opinion Expression and Consensus Building using Future Chronicle

It is an important issue to understand the mechanism of personal opinion expression and consensus building in communities. We implemented an interactive application called ldquofuture chroniclerdquo for monitoring personal opinion expression and consensus building in communities. Future chronicle produces the field of personal opinion expression from multiple users, and users can share their thoughts with each other on this field. Moreover, we can observe the growth of communities through this application. In this paper, we report our experiments using future chronicle.

Multi-user-type travel simulator based on open travel data

We have developed a simulator for evaluating changes in travel quality experienced during travel in multi-modal transit systems. The simulator has a new behavior model for users of multimodal systems that includes changes of user satisfaction, route and mode choices during travel, and interaction with on-line route guidance. This paper proposes a method for implementing user behavior models based on travel survey data, and describes an application example using open data for the city of Sydney.