Lixiong Chen, Monika Buscher, & Yang Hu. (2020). Crowding Out the Crowd:The Transformation of Network Disaster Communication Patterns on Weibo. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 472–489). Blacksburg, VA (USA): Virginia Tech.
Abstract: There is a surge in people turning to social media in disasters in China. In the 2010 Yushu earthquake, 5,979 Weibos were posted. Almost 10 years on, in the 2019 Yibin earthquake it was 17,495. This study presents a Social Network Analysis of the dynamics of this growth, taking the six major Chinese earthquakes of this decade as a case study. By constructing relationship matrices, the research reveals a transformation of networked crisis communication patterns on Weibo. We show how communication relationships between verified organisational users, government agencies, verified individual users (such as celebrities) and unverified ordinary users have changed, and we observe that government agencies are 'crowding out the crowd' of other users. We consider key aspects and the ethical complexities of this phenomenon.
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Toshihiro Osaragi. (2018). Crowding of Various Facilities Relevant to Supporting People Who Have Difficulty Returning Home after a Large Earthquake. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 45–59). Rochester, NY (USA): Rochester Institute of Technology.
Abstract: When a large earthquake occurs, many people are presumed to have difficulty in returning home. However, no research has been achieved yet to discuss the congestion of supporting facilities for stranded people in terms of site, the number and spatial distribution. In this study, we construct a simulation model, which describes people's behavior such as returning home or going to other facilities after an earthquake occurs. Using the model, we estimate the congestion of facilities which varies according to day of the week or the time when the event occurs, and demonstrate the effective methods for reducing the congestion, which include offering information for people and cooperation of private institutions.
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