Kosuke Shinoda

The possibility of social media analysis for disaster management

Collecting, sharing, and delivering information in disaster situations is crucially important. Mass media such as TV, radio, and newspapers have played important roles in information distribution in past disasters and crises. Recently, social media have received much attention for their use as an information sharing tool. Especially, it is said that people used Twitter to collect and share information in the aftermath of the Great East Japan Earthquake. In academic fields, some researchers have started to propose some methods and systems for disaster management by analyzing social media data. Other people doubt whether social media will actually function effectively for disaster management because of uncertainty and inaccuracies related to rumors and misunderstanding. In this paper, we overview current studies of social media analysis for disaster management and explain some studies in detail to show their possibility and availability. We specifically examine situational awareness, user behavior analysis and information propagation analysis, which are three approaches to social media analysis, to clarify what social media analysis can and cannot do. Additionally, we propose some concepts for social media analysis and show how those concepts help to collaborate with us, researchers in social media analysis fields and other research fields.

AI Wolf Contest — Development of Game AI Using Collective Intelligence —

In this study, we specify the design of an artificial intelligence (AI) player for a communication game called “Are You a Werewolf?” (AI Wolf). We present the Werewolf game as a standard game problem in the AI field. It is similar to game problems such as Chess, Shogi, Go, and Poker. The Werewolf game is a communication game that requires several AI technologies such as multi-agent coordination, intentional reading, and understanding of the theory of mind. Analyzing and solving the Werewolf game as a standard problem will provide useful results for our research field and its applications. Similar to the RoboCup project, the goal of this project is to determine new themes while creating a communicative AI player that can play the Werewolf game with humans. As an initial step, we designed a platform to develop a game-playing AI for a competition. First, we discuss the essential factors in Werewolf with reference to other studies. We then develop a platform for an AI game competition that uses simplified rules to support the development of AIs that can play Werewolf. The paper reports the process and analysis of the results of the competition.

Generating human-like discussion by paraphrasing a translation by the AIWolf protocol using werewolf BBS logs

“Are you a werewolf?” is one of the most popular communication games and is played globally. The AIWolf Project developed an agent, named “theAIWolf,” that can play “Areyou a werewolf?”. An AIWolf utters its thoughts using an AIWolf Protocol. As it is difficult for humans to understand the AIWolf Protocol, translation into natural language is required when human players are involved. However, the conventional method of translation uses a word-to-word method, creating the impression that the utterances have been generated by a machine. This study aimed make the utterances of AIWolf sound more human. The authors set the target that a human player would be unable to distinguish human speech from that generated by AIWolves (the Turing test). The authors define the situation as the maximum value of humanity. The output of translated AIWolf Protocol was paraphrased using data from Werewolf BBS Logs. This study considers making the utterances of AIWolf sound more human using Werewolf BBS Logs and a possibility assignment equation with fuzzy sets. In this paper, an experiment was conducted to confirm whether paraphrasing the utterances of AIWolf using Werewolf BBS Logs for human-like speech is useful or not. It was shown that the experimental method produced slightly more human-like speech than the conventional method.

Mining User Experience through Crowdsourcing: A Property Search Behavior Corpus Derived from Microblogging Timelines

This article describes how to build a property search behavior corpus derived from microblogging timelines, in which tweets related to property search are annotated. We applied microtask-based crowdsourcing to tweet data, and build a corpus which consists of timelines of specific users which are annotated with property search stages (e.g. gathering of property information, and property preview). As a result, property search processes by tens of people were annotated. This corpus is intended to use for redesigning property information services, and marketing information services for potential users.