Twitter is a famous social networking service and has received attention recently. Twitter user have increased rapidly, and many users exchange information. When 2011 Tohoku earthquake and tsunami happened, people were able to obtain information from social networking service. Though Twitter played the important role, one of the problem of Twitter, a false rumor diffusion, was pointed out. In this research, we focus on a false rumor diffusion. We propose a information diffusion model based on SIR model, classify the way of diffusion in four categories, and reapper the real diffussion by using this new model.
Models that estimate latent classes for movie recommendation based on PLSA and decision trees are proposed. Proposed model can explain the reason why such recommendation results are given. Using proposed model for so-called cold start problems in recommendation, we can handle the users who don't have enough records. Instead of conventional PLSA for recommendation, we use decision tree models consist of some questions. So, instead of using user's records, we can recommend suitable movies using user's answers as input of decision trees. In an experiment of questionnaire survey, improvement of the satisfaction of the proposal is 45% in comparison with the previous method by showing the recommendation reason. Another experiment is implemented where the users who have less than 9 movie viewing are recommended more appropriate movies after answering 5 questionnaires.
This paper analyzes the vulunerability of firms transaction networks empirically. First we try to reproduce the product and money flows on the network using firms' attributes and input-output table. Then we apply the flows to large real transaction dataset. Finally, we identify the most critical firms and industries for the network vulunerability.
We have a project for the Smart City Hakodate. The goal is to realize the public transportation called the Smart Access Vehicle System, which provides traffic vehicles on demand from users. In this paper, we focus on the reports recording individual traffic behaviors for a traffic flow simulator in Hakodate.
We developed O2, an e-participation web platform, that facilitates public involvement by utilizing background information behind regional social issues gathered fromWeb. The platform is developed on the basis of a Linked Open Dataset called SOCIA, which contains web news articles, tweets, and meeting minutes related to geographic regions. Since Japanese regional communities face complicated and ongoing social issues, there is an urgent need to develop technology for sharing background information and facilitating public debate. This paper presents a roadmap to practical application of our platform in regional communities and discusses remaining requirements.