Abstract：
:The Shanghai New Year's Eve stampede on 31 December 2014, caused 36 deaths and 47 other injuries, generating attention from around the world. This research aims to explore crowd aggregation from the perspective of Sina Weibo check-in data and evaluate the potential of crowd detection based on social media data. We develop a framework using Weibo check-in data in three dimensions: the aggregation level of check-in data, the topic changes in posts and the sentiment fluctuations of citizens. The results show that the numbers of check-ins in all of Shanghai on New Years' Eve is twice that of other days and that Moran's I reaches a peak on this date, implying a spatial autocorrelation mode. Additionally, the results of topic modeling indicate that 72.4% of the posts were related to the stampede, reflecting public attitudes and views on this incident from multiple angles. Moreover, sentiment analysis based on Weibo posts illustrates that the proportion of negative posts increased both when the stampede occurred (40.95%) and a few hours afterwards (44.33%). This study demonstrates the potential of using geotagged social media data to analyze population spatiotemporal activities, especially in emergencies.
journal_name
Int J Environ Res Public Healthauthors
Duan J,Zhai W,Cheng Cdoi
10.3390/ijerph17228640subject
Has Abstractpub_date
2020-11-20 00:00:00issue
22eissn
1661-7827issn
1660-4601pii
ijerph17228640journal_volume
17pub_type
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journal_title：International journal of environmental research and public health
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pub_type： 杂志文章,随机对照试验
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更新日期：2020-10-17 00:00:00
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更新日期：2018-09-03 00:00:00
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更新日期：2019-10-12 00:00:00
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更新日期：2019-08-27 00:00:00
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更新日期：2019-09-29 00:00:00
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