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Although election campaigns are increasingly utilizing social media, only a few studies have investigated their effects experimentally. To fill this gap in the literature, we conducted a field experiment to examine the effects of a campaign that used Twitter during the 2013 House of Councillors election in Japan. The treatment was exposure to tweets from Tōru Hashimoto, the mayor of Osaka and co-leader of the Japan Restoration Party, who has the largest number of Twitter followers among Japanese politicians. Participants assigned to the treatment group followed Hashimoto and the two placebos, whereas those assigned to the control condition followed only the two placebos. They followed the politicians continuously for approximately one month. Pre-and posttreatment measures were collected using online surveys, and treatment compliance was continuously checked via Twitter application programming interface (API). Following Hashimoto on Twitter during the election campaign had a positive impact on feelings toward Hashimoto. This effect was not mediated by issue knowledge or the evaluation of Hashimoto's personal traits, and no effects were observed on voting. These findings suggest that repeated exposure to a politician's messages on Twitter may only result in a mere exposure effect, which nevertheless generates favorable overall attitudes about the politician.

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Public policy should be decided quickly on the basis of scientific data. However, in urgent situations, e.g., disaster recovery, it takes too long to administer a social survey and collect the results. We have therefore developed a social data collection technique that utilizes the Web reservation data of hotels and bullet trains and propose using this technique to support policymaking in real time. One challenge with using Web data is the difficulty of grasping the real situation due to problems with duplication, so we came up with a method of integrating Web reservation data. We built a social data collection infrastructure using Web data and then compared the integrated data with social survey data of Kyoto and Sendai. Results showed that the integrated data fit the social survey data within 10%. Using these data can show the resilience of hotels and shinkansen. By performing analysis on the basis of this collected data, we can support more timely policymaking. This infrastructure is effective both normal situation and disaster situation.

The Cyber-Physical Integrated Society (CPIS) is being formed with the fusion of cyber-space and the real-world. In this paper, we will discuss Data-Driven Decision-Making (DDDM) support systems to solve social problems in the CPIS. First, we introduce a Web of Resources (WoR) that uses Web booking log data for destination data management. Next, we introduce an Internet of Persons (IoP) system to visualize individual and group flows of people by analyzing collected Wi-Fi usage log data. Specifically, we present examples of how WoR and IoP visualize flows of groups of people that can be shared across different industries, including telecommunications carriers and railway operators, and policy decision support for local, short-term events. Finally, the importance of data-driven training of human resources to support DDDM in the future CPIS is discussed.