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Information 2018, 9(10), 257; https://doi.org/10.3390/info9100257

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

1
School of Communication & Information Engineering, Shanghai University, Shanghai, 200444, China
2
Institute of Smart City, Shanghai University, Shanghai, 200444, China
*
Author to whom correspondence should be addressed.
Received: 12 September 2018 / Revised: 10 October 2018 / Accepted: 11 October 2018 / Published: 20 October 2018
(This article belongs to the Special Issue Information Management in Information Age)
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Abstract

With rapid advancement in location-based services (LBS), their acquisition has become a powerful tool to link people with similar interests across long distances, as well as connecting family and friends. To observe human behavior towards using social media, it is essential to understand and measure the check-in behavior towards a location-based social network (LBSN). This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on the frequency of using an LBSN. In this paper, we investigate the check-in behavior of several million individuals, for whom we observe the gender and their frequency of using Chinese microblog Sina Weibo (referred as “Weibo”) over a period in Shanghai, China. To produce a smooth density surface of check-ins, we analyze the overall spatial patterns by using the kernel density estimation (KDE) by using ArcGIS. Furthermore, our results reveal that female users are more inclined towards using social media, and a difference in check-in behavior during weekday and weekend is also observed. From the results, LBSN data seems to be a complement to traditional methods (i.e., survey, census) and is used to study gender-based check-in behavior. View Full-Text
Keywords: social media; LBSN; check-in; gender; time; behavior; geolocation social media; LBSN; check-in; gender; time; behavior; geolocation
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Rizwan, M.; Wan, W. Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data. Information 2018, 9, 257.

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