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.
Highlights
Human mobility and human behavior towards services are closely intertwined with personal behavior and characteristics
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 observed
location-based social network (LBSN) data seems to be a complement to traditional methods and is used to study gender-based check-in behavior
Summary
Human mobility and human behavior towards services are closely intertwined with personal behavior and characteristics. Compared to the aforementioned traditional methods, LBSN data are highly available at low cost This data contains rich information about geolocation [18], which can be used to study check-in behavior. Geo-location data offers new dimensions towards studying check-in behaviors and can help to create new techniques and approaches to analyze LBSN data. It seems that LBSN data can be a supplement to, rather than a substitute for, traditional data sources.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
