Yuya Shibuya

Bio: Yuya Shibuya is an academic researcher from University of Tokyo. The author has contributed to research in topics: Social media & Disaster recovery. The author has an hindex of 4, co-authored 18 publications receiving 44 citations.

Using Social Media to Detect Socio-Economic Disaster Recovery

Abstract: 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 activities as reflected in market data. Our result shows multiple correlations between sentiment on social media and the socio-economic recovery activities involved in restarting daily routines. Conventional socio-economic recovery indicators, such as governmental statistical data, have a significant time lag before publishing. Therefore, by taking advantages of the real timeliness and the effectiveness of seizing communication trends of massive social media data, using public sentiment on social media can improve situational awareness in recovery operations.

What Makes Civic Tech Initiatives To Last Over Time? Dissecting Two Global Cases

Abstract: Civic tech initiatives dedicated to environmental issues have become a worldwide phenomenon and made invaluable contributions to data, community building, and publics. However, many of them stop after a relatively short time. Therefore, we studied two long-lasting civic tech initiatives of global scale, to understand what makes them sustain over time. To this end, we conducted two mixed-method case studies, combining social network analysis and qualitative content analysis of Twitter data with insights from expert interviews. Drawing on our findings, we identified a set of key factors that help the studied civic tech initiatives to grow and last. Contributing to Digital Civics in HCI, we argue that the civic tech initiatives’ scaling and sustaining are configured through the entanglement of (1) civic data both captured and owned by the citizens for the citizens, (2) the use of open and accessible technology, and (3) the initiatives’ public narrative, giving them a voice on the environmental issue.

Mining social media for disaster management: Leveraging social media data for community recovery

Abstract: 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.

How does a large-scale disaster impact on the used-car market? A case study of the Great East Japan Earthquake and Tsunami

Abstract: The aim of this paper is to examine how a large-scale natural disaster impacts on the used-car market. This paper particularly tries to clarify what type of cars and what kinds of car features were demanded in damaged areas and when. We conduct a case study on the Great East Japan Earthquake and Tsunami of 2011 and compare the used-car prices in damaged areas and non-damaged areas. The finding of this paper is that after the Great East Japan Earthquake and Tsunami, cheaper used cars and used cars with larger carrying capacity were needed in damaged areas. In addition, we find that car types which people in the damaged areas needed depend on the phase of recovery.

A Statistical Analysis Between Consumer Behavior and a Social Network Service: A Case Study of Used-Car Demand Following the Great East Japan Earthquake and Tsunami of 2011

Abstract: When a large-scale disaster hits a community, especially a water-related disaster, there is a scarcity of automobiles and a sudden increase in the demand for used cars in the damaged areas. This paper conducts a case study of a recent massive natural disaster, the Great East Japan Earthquake and Tsunami of 2011 to understand those car scarcities and demand in the aftermath of the catastrophe. We analyze the reasons for the increase in demand for used cars and how social media can predict people’s demand for used automobiles. In other words, this paper explores whether social media data can be used as a sensor of socio-economic recovery status in damaged areas during large-scale water-related disaster-recovery phases. For this purpose, we use social media communication as a proxy for estimating indicators of people’s activities in the real world. This study conducts both qualitative analysis and quantitative analysis. For the qualitative research, we carry out semi-structured interviews with used-car dealers in the tsunami-stricken area and unveil why people in the area demanded used cars. For the quantitative analysis, we collected Facebook page communication data and used-car market data before and after the Great East Japan Earthquake and Tsunami of 2011. By combining and analyzing these two types of data, we find that social media communication correlates with people’s activities in the real world. Furthermore, this study suggests that different types of communication on social media have different types of correlations with people’s activities. More precisely, we find that social media communication related to people’s activities for rebuilding and for emotional support is positively correlated with the demand for used cars after the Great East Japan Earthquake and Tsunami. On the other hand, communication about anxiety and information seeking correlates negatively with the demand for used cars.

The potential of emergent disruptive technologies for humanitarian supply chains: the integration of blockchain, Artificial Intelligence and 3D printing

Abstract: The growing importance of humanitarian operations has created an imperative to overcome the complications currently recorded in the field. Challenges such as delays, congestion, poor communication ...

Applications of artificial intelligence for disaster management

Abstract: Natural hazards have the potential to cause catastrophic damage and significant socioeconomic loss. The actual damage and loss observed in the recent decades has shown an increasing trend. As a result, disaster managers need to take a growing responsibility to proactively protect their communities by developing efficient management strategies. A number of research studies apply artificial intelligence (AI) techniques to process disaster-related data for supporting informed disaster management. This study provides an overview of current applications of AI in disaster management during its four phases: mitigation, preparedness, response, and recovery. It presents example applications of different AI techniques and their benefits for supporting disaster management at different phases, as well as some practical AI-based decision support tools. We find that the majority of AI applications focus on the disaster response phase. This study also identifies challenges to inspire the professional community to advance AI techniques for addressing them in future research.

Transportation sentiment analysis using word embedding and ontology-based topic modeling

Abstract: Social networks play a key role in providing a new approach to collecting information regarding mobility and transportation services. To study this information, sentiment analysis can make decent observations to support intelligent transportation systems (ITSs) in examining traffic control and management systems. However, sentiment analysis faces technical challenges: extracting meaningful information from social network platforms, and the transformation of extracted data into valuable information. In addition, accurate topic modeling and document representation are other challenging tasks in sentiment analysis. We propose an ontology and latent Dirichlet allocation (OLDA)-based topic modeling and word embedding approach for sentiment classification. The proposed system retrieves transportation content from social networks, removes irrelevant content to extract meaningful information, and generates topics and features from extracted data using OLDA. It also represents documents using word embedding techniques, and then employs lexicon-based approaches to enhance the accuracy of the word embedding model. The proposed ontology and the intelligent model are developed using Web Ontology Language and Java, respectively. Machine learning classifiers are used to evaluate the proposed word embedding system. The method achieves accuracy of 93%, which shows that the proposed approach is effective for sentiment classification.

Location reference identification from tweets during emergencies: A deep learning approach

Abstract: Twitter is recently being used during crises to communicate with officials and provide rescue and relief operation in real time. The geographical location information of the event, as well as users, are vitally important in such scenarios. The identification of geographic location is one of the challenging tasks as the location information fields, such as user location and place name of tweets are not reliable. The extraction of location information from tweet text is difficult as it contains a lot of non-standard English, grammatical errors, spelling mistakes, non-standard abbreviations, and so on. This research aims to extract location words used in the tweet using a Convolutional Neural Network (CNN) based model. We achieved the exact matching score of 0.929, Hamming loss of 0.002, and F 1 -score of 0.96 for the tweets related to the earthquake. Our model was able to extract even three- to four-word long location references which is also evident from the exact matching score of over 92%. The findings of this paper can help in early event localization, emergency situations, real-time road traffic management, localized advertisement, and in various location-based services.

Fuzzy Ontology and LSTM-Based Text Mining: A Transportation Network Monitoring System for Assisting Travel

Abstract: Intelligent Transportation Systems (ITSs) utilize a sensor network-based system to gather and interpret traffic information. In addition, mobility users utilize mobile applications to collect transport information for safe traveling. However, these types of information are not sufficient to examine all aspects of the transportation networks. Therefore, both ITSs and mobility users need a smart approach and social media data, which can help ITSs examine transport services, support traffic and control management, and help mobility users travel safely. People utilize social networks to share their thoughts and opinions regarding transportation, which are useful for ITSs and travelers. However, user-generated text on social media is short in length, unstructured, and covers a broad range of dynamic topics. The application of recent Machine Learning (ML) approach is inefficient for extracting relevant features from unstructured data, detecting word polarity of features, and classifying the sentiment of features correctly. In addition, ML classifiers consistently miss the semantic feature of the word meaning. A novel fuzzy ontology-based semantic knowledge with Word2vec model is proposed to improve the task of transportation features extraction and text classification using the Bi-directional Long Short-Term Memory (Bi-LSTM) approach. The proposed fuzzy ontology describes semantic knowledge about entities and features and their relation in the transportation domain. Fuzzy ontology and smart methodology are developed in Web Ontology Language and Java, respectively. By utilizing word embedding with fuzzy ontology as a representation of text, Bi-LSTM shows satisfactory improvement in both the extraction of features and the classification of the unstructured text of social media.