Yuya Shibuya

In the last decades, researchers in Human-computer interaction (HCI) took numerous efforts to investigate people's social media interaction. To understand how researchers in HCI have studied social media interaction, we examined 149 peer-reviewed articles published between 2008 and 2020 in major HCI conference proceedings and journals. We systematically reviewed the methodologies HCI researchers applied, the research topics these methods covered, and the types of data collected. Through the anal

There has been growing interest in harnessing Artificial Intelligence (AI) to improve situational awareness for disaster management. However, to the authors’ best knowledge, few studies have focused on socio-economic recovery. Here, as a first step toward investigating the possibility of developing an AI-based method for detecting socio-economic recovery, this study provides fundamental insights about the correlations between public sentiment on social media and socio-economic recovery activitie

Emotions are the basis of human behavior and an important part of spatial constructions. Despite numerous efforts to leverage spatio-temporal big data from social media to capture public sentiments, there is a lack of understanding of how individuals' behaviors play a role in people's sentiments at places they visit. Thus, this study aims to advance our understanding of the complex relationships between sentiments, behaviors, and places. Through a case study in Japan in 2021, we analyzed one yea

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 disas

Data are essential for digital solutions and supporting citizens' everyday behavior. Open data initiatives have expanded worldwide in the last decades, yet investigating the actual usage of open data and evaluating their impacts are insufficient. Thus, in this paper, we examine an exemplary use case of open data during the early stage of the Covid-19 pandemic and assess its impacts on citizens. Based on quasi-experimental methods, the study found that publishing local stores' real-time face mask

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 dis

Given the rise of concerns about Generative Artificial Intelligence (GenAI) powered misinformation, major platforms like Google, Meta, and TikTok have implemented new policies to warn users of AI-generated content. However, we have not fully understood the impacts of such user interface designs that disclose AI made content on user perceptions. This study investigates how people assess the accuracy of video content when they are warned that it is created by GenAI. We conducted an online experime

There have been various studies analyzing public sentiment after a large-scale disaster. However, few studies have focused on the relationship between public sentiment on social media and its results on people's activities in the real world. In this paper, we conduct a long-term sentiment analysis after the Great East Japan Earthquake and Tsunami of 2011 using Facebook Pages with the aim of investigating the correlation between public sentiment and people's actual needs in areas damaged by water

The goal of this paper is to examine the possibility of using big data in housing market as one of socio-economic recovery indicators. Disaster recovery is a complex process and should be captured via multiple angles. Here, we explore the possibility of using online big data in real estate to detect one of socio-economic recovery activities. This study focuses on big data because its characteristics, such as timeliness, have the feasibility of complementing the traditional socio-economic recover