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Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events

1
Chair of Cartography, Technical University of Munich, 80333 Munich, Germany
2
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3
School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China
4
KRDB Research Centre, Faculty of Computer Science, Free University of Bozen-Bolzano, 39100 Bolzano, Italy
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(1), 15; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8010015
Received: 19 October 2018 / Revised: 19 December 2018 / Accepted: 20 December 2018 / Published: 29 December 2018
Studying the impact of social events is important for the sustainable development of society. Given the growing popularity of social media applications, social sensing networks with users acting as smart social sensors provide a unique channel for understanding social events. Current research on social events through geo-tagged social media is mainly focused on the extraction of information about when, where, and what happened, i.e., event detection. There is a trend towards the machine learning of more complex events from even larger input data. This research work will undoubtedly lead to a better understanding of big geo-data. In this study, however, we start from known or detected events, raising further questions on how they happened, how they affect people’s lives, and for how long. By combining machine learning, natural language processing, and visualization methods in a generic analytical framework, we attempt to interpret the impact of known social events from the dimensions of time, space, and semantics based on geo-tagged social media data. The whole analysis process consists of four parts: (1) preprocessing; (2) extraction of event-related information; (3) analysis of event impact; and (4) visualization. We conducted a case study on the “2014 Shanghai Stampede” event on the basis of Chinese Sina Weibo data. The results are visualized in various ways, thus ensuring the feasibility and effectiveness of our proposed framework. Both the methods and the case study can serve as decision references for situational awareness and city management. View Full-Text
Keywords: social sensing; machine learning; social opinion mining; topic discovery; visual analysis social sensing; machine learning; social opinion mining; topic discovery; visual analysis
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MDPI and ACS Style

Zhu, R.; Lin, D.; Jendryke, M.; Zuo, C.; Ding, L.; Meng, L. Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events. ISPRS Int. J. Geo-Inf. 2019, 8, 15. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8010015

AMA Style

Zhu R, Lin D, Jendryke M, Zuo C, Ding L, Meng L. Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events. ISPRS International Journal of Geo-Information. 2019; 8(1):15. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8010015

Chicago/Turabian Style

Zhu, Ruoxin, Diao Lin, Michael Jendryke, Chenyu Zuo, Linfang Ding, and Liqiu Meng. 2019. "Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events" ISPRS International Journal of Geo-Information 8, no. 1: 15. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8010015

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