I've been engaged in empirical research in a wide range of socio-information studies, spatial informatics, and HCI (Human Computer Interaction) fields, focusing on the themes listed in the research map above. A list of some major research projects is provided below (not an exhaustive list).
Understanding the current state and what would happen when something was changed, introduced, or removed plays a key role in discussing, designing, and planning sustainable transitions. Advanced technologies and data enable us to analyze as many scenarios as possible before implementing policies or introducing infrastructures in urban spaces. We address the following research questions in order to make such solutions more equitable, diverse, and inclusive for broader cities and villages: (1) how to design participatory structures through digital solutions in cities and villages, (2) how to simulate human behavior changes in cities and villages with considering socio-economic heterogeneity, and (3) how to estimate and evaluate human behavior changes in urban spaces.
Shibuya, Yuya, Chun-Ming Lai, Andrea Hamm, Soichiro Takagi, and Yoshihide Sekimoto. (2022). Do Open Data Impact Citizens’ Behavior? Assessing Face Mask Panic Buying Behaviors during the Covid-19 Pandemic. Scientific Reports. 12 (1): 17607. https://doi.org/10.1038/s41598-022-22471-y.
Hamm, Andrea, Shibuya, Yuya, Teresa, Cerratto Pargman, Bendor, Roy, Brodersen-Hansen, Nicolai, Raetzsch, Christoph, Shoji, Masahiko, Bieber, Christoph, Hendawy, Mennatullah, Klerks, Gwen, & Schouten, Ben. (2023). Failed yet successful: Learning from discontinued civic tech initiatives. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA ’23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA.
Shibuya, Yuya, Hamm, Andrea, & Raetzsch, Christoph. (2021). From Data to Discourse: How Communicating Civic Data Can Provide a Participatory Structure for Sustainable Cities and Communities. Proceedings of the International Sustainable Development Research Society (ISDRS), July 13-15, 2021, Sweden.
Hamm, Andrea, Shibuya, Yuya, Ullrich, Stefan, & Cerratto Pargman, Teresa. (2021). What Makes Civic Tech Initiatives To Last Over Time? Dissecting Two Global Cases. In CHI ’21: Proceedings of the 39th Annual ACM Conference on Human Factors in Computing Systems, May 08–13, 2021, Yokohama, Japan. ACM, New York, NY, USA
Use of email has remained a prominent communication tool in business environments, making email security crucial for every organization. However, the decision-making process in setting security policies is not always straightforward. Rather than prioritizing actual effectiveness and reliability, organizations sometimes adopt security practices based on human factors, such as ease of use. Consequently, various seemingly effective yet ineffective security measures have been observed, creating a situation known as security theater. To deepen our understanding of security-related decision-making in organizations, we scrutinize an email security practice in Japan as a case of security theater. We ask why such an ineffective practice has become rampant and why organizations cannot switch from it despite recognizing its ineffectiveness. Our study emphasizes that the pseudo effects of security measures (e.g., the visibility of security procedures) and peer pressure (e.g., competitors’ and business partners’ practices) have more associations with the organizations’ decision-making practices than actual effectiveness, highlighting the key role of human factors in designing email security solutions.
The study explores the possibility of using social media data for detecting socio-economic recovery activities. In the last decade, there have been intensive research activities focusing on social media during and after disasters. This approach, which views people's communication on social media as a sensor for real-time situations, has been widely adopted as the "people as sensor" approach. Furthermore, detecting communities' real-time recovery situations is essential to improve recovery efforts after large-scale disasters since conventional socio-economic recovery indicators, such as governmental statistics, are not published in real-time. Thanks to its timeliness, using social media data can fill the gap. Motivated by this possibility, the study primarily focuses on the relationships between people's communication on Twitter and Facebook pages and socio-economic recovery activities as reflected in the used-car market data and the housing market data in the case of two major disasters: the Great East Japan Earthquake and Tsunami of 2011 and Hurricane Sandy in 2012. The study sheds light on the "people as sensors" approach for detecting socio-economic recovery activities, which has not been thoroughly studied to date but can potentially improve situation awareness during the recovery phase. Secondly, the study proposes new socio-economic recovery indicators: social media communication data, used-car market data, and housing market data. Thirdly, in the context of using social media during the recovery phase, the results demonstrate the importance of distinguishing people behind communications. The study demonstrates the importance of taking into consideration geological closeness and people's personal relations to the affected areas (i.e., whether communications are posted by people who are at or near disaster-stricken areas and by those who are farther away).
Shibuya, Yuya, (2020). Social Media Communication Data for Recovery: Detecting Socio-Economic Activities Following a Disaster. Springer, DOI:10.1007/978-981-15-0825-7.
Shibuya, Yuya & Tanaka, H. (2019), Detecting Disaster Recovery Activities via Social Media Communication Topics. In Proceedings of the 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019), 706-718, May 19-22nd 2019. Valencia, Spain.
Shibuya, Yuya & Tanaka, H., (2019). Using Social Media to Detect Socio-Economic Disaster Recovery. IEEE Intelligent Systems, May/June 2019, 34(3), 29-37.
Shibuya, Yuya. (2017). Mining Social Media for Disaster Management: Leveraging Social Media Data for Community Recovery, In Proceedings of IEEE Big Data workshop on the 2nd International workshop on application of big data for computational social science (Dec. 2017), Boston, MA. 3029-3036.
Social Media Interaction
Shibuya, Yuya, Hamm, Andrea, & Cerratto Pargman, Teresa. (2022). Mapping HCI Research Methods for Studying Social Media Interaction: A Systematic Literature Review. Computers in Human Behavior, 129, 107131.