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

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...

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Bibliographic Details
Published in:Information (Basel) 2018-10, Vol.9 (10), p.257
Main Authors: Rizwan, Muhammad, Wan, Wanggen
Format: Article
Language:English
Subjects:
behavior
check-in
Data analysis
Datasets
Density
Digital media
gender
Gender differences
geolocation
Human behavior
LBSN
Location based services
social media
Social networks
time
User behavior
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Summary: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.
ISSN:2078-2489
2078-2489
DOI:10.3390/info9100257