Yuya Shibuya

Dr. Yuya Shibuya

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Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 April 2022 in Computers in Human Behavior
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Journal Article
Open access
YSYuya Shibuya
Yuya Shibuya
AHAndrea Hamm
Andrea Hamm
TPTeresa Cerratto Pargman
Teresa Cerratto Pargman

30 May 2019 in IEEE Intelligent Systems
Verified
Journal Article
Closed access
YSYuya Shibuya
Yuya Shibuya
HTHideyuki Tanaka
Hideyuki Tanaka

13 March 2025 in International Journal of Human-Computer Studies
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Journal Article
Open access
YSYuya Shibuya
Yuya Shibuya
TNTomoka Nakazato
Tomoka Nakazato
STSoichiro Takagi
Soichiro Takagi

1 December 2017
Verified
Conference Paper
Closed access
YSYuya Shibuya
Yuya Shibuya
Social media data, from Twitter and Facebook, for example, can be regarded as critical information sources during disasters through their use in detecting and assessing disaster situations. This study overviews relevant literature from the perspective of social media for disaster management. The findings of this study show that while many previous studies have focused on how to leverage social media data for mitigating and responding to disasters, few have focused on social media use for a disaster-struck community's recovery. This paper also argues that there is a need to study the correlations between social media data and the affected people's recovery activities in the real world. With this gap in mind, the author discusses one potential avenue for future work.

15 May 2024 in Scientific Reports
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Journal Article
Open access
PDF file
YSYuya Shibuya
Yuya Shibuya
NJNicholas Jones
Nicholas Jones
YSYoshihide Sekimoto
Yoshihide Sekimoto
Given the worldwide increase of forcibly displaced populations, particularly internally displaced persons (IDPs), it’s crucial to have an up-to-date and precise tracking framework for population movements. Here, we study how the spatial and temporal pattern of a large-scale internal population movement can be monitored using human mobility datasets by exploring the case of IDPs in Ukraine at the beginning of the Russian invasion of 2022. Specifically, this study examines the sizes and travel distances of internal displacements based on GPS human mobility data, using the combinations of mobility pattern estimation methods such as truncated power law fitting and visualizing the results for humanitarian operations. Our analysis reveals that, although the city of Kyiv started to lose its population around 5 weeks before the invasion, a significant drop happened in the second week of the invasion (4.3 times larger than the size of the population lost in 5 weeks before the invasion), and the population coming to the city increased again from the third week of the invasion, indicating that displaced people started to back to their homes. Meanwhile, adjacent southern areas of Kyiv and the areas close to the western borders experienced many migrants from the first week of the invasion and from the second to third weeks of the invasion, respectively. In addition, people from relatively higher-wealth areas tended to relocate their home locations far away from their original locations compared to those from other areas. For example, 19 % of people who originally lived in higher wealth areas in the North region, including the city of Kyiv, moved their home location more than 500 km, while only 9 % of those who originally lived in lower wealth areas in the North region moved their home location more than 500 km.