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...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Muhammad Rizwan [verfasserIn] Wanggen Wan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	social media LBSN check-in gender time behavior geolocation

Übergeordnetes Werk:	In: Information - MDPI AG, 2010, 9(2018), 10, p 257
Übergeordnetes Werk:	volume:9 ; year:2018 ; number:10, p 257

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/info9100257

Katalog-ID:	DOAJ077110390

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allfieldsSound	10.3390/info9100257 doi (DE-627)DOAJ077110390 (DE-599)DOAJ6ad1f788d6e04f01a5fdb34fb51b9cdf DE-627 ger DE-627 rakwb eng T58.5-58.64 Muhammad Rizwan verfasserin aut Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. social media LBSN check-in gender time behavior geolocation Information technology Wanggen Wan verfasserin aut In Information MDPI AG, 2010 9(2018), 10, p 257 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:9 year:2018 number:10, p 257 https://doi.org/10.3390/info9100257 kostenfrei https://doaj.org/article/6ad1f788d6e04f01a5fdb34fb51b9cdf kostenfrei http://www.mdpi.com/2078-2489/9/10/257 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2018 10, p 257
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callnumber-first	T - Technology
author	Muhammad Rizwan
spellingShingle	Muhammad Rizwan misc T58.5-58.64 misc social media misc LBSN misc check-in misc gender misc time misc behavior misc geolocation misc Information technology Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data
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topic	misc T58.5-58.64 misc social media misc LBSN misc check-in misc gender misc time misc behavior misc geolocation misc Information technology
topic_unstemmed	misc T58.5-58.64 misc social media misc LBSN misc check-in misc gender misc time misc behavior misc geolocation misc Information technology
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title	Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data
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title_auth	Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data
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