Kousuke Shinoda

Information sharing on Twitter during the 2011 catastrophic earthquake

Such large disasters as earthquakes and hurricanes are very unpredictable. During a disaster, we must collect information to save lives. However, in time disaster, it is difficult to collect information which is useful for ourselves from such traditional mass media as TV and newspapers that contain information for the general public. Social media attract attention for sharing information, especially Twitter, which is a hugely popular social medium that is now being used during disasters. In this paper, we focus on the information sharing behaviors on Twitter during disasters. We collected data before and during the Great East Japan Earthquake and arrived at the following conclusions: Many users with little experience with such specific functions as reply and retweet did not continuously use them after the disaster. Retweets were well used to share information on Twitter. Retweets were used not only for sharing the information provided by general users but used for relaying the information from the mass media. We conclude that social media users changed their behavior to widely diffuse important information and decreased non-emergency tweets to avoid interrupting critical information.

Usability of dial-a-ride systems

A case study of usability of dial-a-ride bus systems is reported. We conduct a social simulation to compare efficiencies of the dial-a-ride bus systems, one of possible multi-agent applications, and traditional fixed-route bus systems. Simulation results indicated that dial-a-ride systems are reasonable for large cities but their advantage depends on structures of the town.

Classification Method for Shared Information on Twitter Without Text Data

During a disaster, appropriate information must be collected. For example, victims and survivors require information about shelter locations and dangerous points or advice about protecting themselves. Rescuers need information about the details of volunteer activities and supplies, especially potential shortages. However, collecting such localized information is difficult from such mass media as TV and newspapers because they generally focus on information aimed at the general public. On the other hand, social media can attract more attention than mass media under these circumstances since they can provide such localized information. In this paper, we focus on Twitter, one of the most influential social media, as a source of local information. By assuming that users who retweet the same tweet are interested in the same topic, we can classify tweets that are required by users with similar interests based on retweets. Thus, we propose a novel tweet classification method that focuses on retweets without text mining. We linked tweets based on retweets to make a retweet network that connects similar tweets and extracted clusters that contain similar tweets from the constructed network by our clustering method. We also subjectively verified the validity of our proposed classification method. Our experiment verified that the ratio of the clusters whose tweets are mutually similar in the cluster to all clusters is very high and the similarities in each cluster are obvious. Finally, we calculated the linguistic similarities of the results to clarify our proposed methods features. Our method classified topic-similar tweets, even if they are not linguistically similar.

Investigation of the effects of nonverbal information on werewolf

Werewolf is one of the popular communication games all over the world. It treats ambiguity of human discussion including the utterances, gestures and facial expressions in a broad sense. In this research, we pay attention to this imperfect information game werewolf. The purpose of the research is to develop an intelligent agent “AI werewolf” which is enabled to naturally play werewolf with human. This paper aims to investigate how behavior contribute to victory of own-side players by using machine learning as a first step. As the results of investigation and analysis of the playing movie, we found that nonverbal information in the game of werewolf has importance to winning or losing the game.

Movement design of a life-like agent for the werewolf game

In this research, we target at the interactive communication game “werewolf” with a subject of research. Werewolf is a popular party game all over the world, and the relevance studies have been advanced in recent years. However, the life-like agent who does werewolf has not been developed. Therefore the purpose of this research is to analyze non-verbal information from movies which play the werewolf with face-to-face communication and to make clear the impression for others by the movements of players in the game. Moreover, we verify whether the life-like agent gives an impression like human in werewolf game by mounting the movements on a life-like agent.

Multi-agent Information Diffusion Model for Twitter

During the 2011 East Japan Great Earthquake Disaster, many people used social media such as Twitter to get important information for their lives. But, generally, social media also has bad side, that is wrong information diffusion problem. In this paper, we will propose a novel multiagent-based information diffusion model, the Agent-based Information Diffusion Model (AIDM), and evaluate it. Up to now, our previous model is based on the SIR model, which is famous as a diffusion model of infection. The SIR model is represented by the stochastic state transition model for whether to propagate the information, and its transition probability is defined as the same value for all agents. However, peoples thinking or actions are not the same. To make persons character heterogeneously, we adopted two elements in our proposal model: user diversity and multiplexing of information paths. From a comparison evaluation, it is shown that the proposed model basically to reproduce the information diffusion as same as the diffusion of real data.

SIR-Extended Information Diffusion Model of False Rumor and its Prevention Strategy for Twitter

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.

Regional analysis of user interactions on social media in times of disaster

Social media attract attention for sharing information, especially Twitter, which is now being used in times of disasters. In this paper, we perform regional analysis of user interactions on Twittter during the Great East Japan Earthquake and arrived at the following two conclusions:People diffused much more information after the earthquake, especially in the heavily-damaged areas; People communicated with nearby users but diffused information posted by distant users. We conclude that social media users changed their behavior to widely diffuse information.

Constructing a Human-like agent for the Werewolf Game using a psychological model based multiple perspectives

In this paper, we focus on the Werewolf Game. The Werewolf Game is an advanced communication-game in which winning or losing is directly linked to ones success or failure in communication. Therefore, we expect exponential developments in artificial intelligence by studying the Werewolf Game. In this current study, we propose a psychological model that considers multiple perspectives to model the play of a human such as inferring the intention of the other side. As one of the psychological models, we constructed a “ones self model” that models the role of others as viewed from their own viewpoint. In addition, to determine whether ones opinion is reliable after inferring others intentions, we also constructed an “others model” that models the role of others as viewed from their viewpoints. Combining these models, we showed through experimentation that a combined approach achieved better results, i.e., higher win percentages.

Werewolf Game Modeling Using Action Probabilities Based on Play Log Analysis

In this study, we construct a non-human agent that can play the werewolf game (i.e., AI wolf) with aims of creating more advanced intelligence and acquire more advanced communication skills for AI-based systems. We therefore constructed a behavioral model using information regarding human players and the decisions made by such players; all such information was obtained from play logs of the werewolf game. To confirm our model, we conducted simulation experiments of the werewolf game using an agent based on our proposed behavioral model, as well as a random agent for comparison. Consequently, we obtained an 81.55% coincidence ratio of agent behavior versus human behavior.