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Article

Applying GIS and Text Mining Methods to Twitter Data to Explore the Spatiotemporal Patterns of Topics of Interest in Kuwait

1
Department of Geography, Kuwait University, Safat 13060, Kuwait
2
Curriculum & Instruction Department, Kuwait University, Kuwait 71423, Kuwait
3
Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
The work was done when Liuqing Li was a PhD student at Virginia Tech.
ISPRS Int. J. Geo-Inf. 2020, 9(12), 702; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120702
Received: 16 September 2020 / Revised: 16 November 2020 / Accepted: 17 November 2020 / Published: 25 November 2020
Researchers have developed various approaches for exploring the spatial information, temporal patterns, and Twitter content in topics of interest in order to generate a better understanding of human behavior; however, few investigations have integrated these three dimensions simultaneously. This study analyzes the content of tweets in order to conduct a spatiotemporal exploration of the main topics of interest in Kuwait in order to provide a deeper understanding of the topics people think about, when they think about them, and where they tweet about them. To this end, we collect, process, and analyze tweets from nearly 120 areas in Kuwait over a 10-month period. The study’s results indicate that religion, emotions, education, and public policy are the most popular topics of interest in Kuwait. Regarding the spatiotemporal analysis, people post more tweets regarding religion on Fridays, a holy day for Muslims in Kuwait. Moreover, people are more likely to tweet about policy and education on weekdays rather than weekends. In contrast, people tweet about emotional expressions more often on weekends. From the spatial perspectives, spatial clustering in topics occurs across the days of the week. The findings are applicable to further topic analysis and similar research in other countries. View Full-Text
Keywords: GIS; text mining; spatiotemporal pattern; topic of interest; Twitter GIS; text mining; spatiotemporal pattern; topic of interest; Twitter
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MDPI and ACS Style

G. Almatar, M.; Alazmi, H.S.; Li, L.; Fox, E.A. Applying GIS and Text Mining Methods to Twitter Data to Explore the Spatiotemporal Patterns of Topics of Interest in Kuwait. ISPRS Int. J. Geo-Inf. 2020, 9, 702. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120702

AMA Style

G. Almatar M, Alazmi HS, Li L, Fox EA. Applying GIS and Text Mining Methods to Twitter Data to Explore the Spatiotemporal Patterns of Topics of Interest in Kuwait. ISPRS International Journal of Geo-Information. 2020; 9(12):702. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120702

Chicago/Turabian Style

G. Almatar, Muhammad, Huda S. Alazmi, Liuqing Li, and Edward A. Fox 2020. "Applying GIS and Text Mining Methods to Twitter Data to Explore the Spatiotemporal Patterns of Topics of Interest in Kuwait" ISPRS International Journal of Geo-Information 9, no. 12: 702. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120702

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