Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Population density and distribution of services represents the growth and demographic shift of the cities. For urban planners, population density and check-in behavior in space and time are vital factors for planning and development of sustainable cities. Location-based social network (LBSN) data seems to be a complement to many traditional methods (i.e., survey, census) and is used to study check-in behavior, human mobility, activity analysis, and social issues within a city. This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on gender difference and frequency of using LBSN. Therefore, in this study, we investigate the check-in behavior of Chinese microblog Sina Weibo (referred as " Weibo ") in 10 districts of Shanghai, China, for which we observe the gender difference and their frequency of use over a period. The mentioned districts were spatially analyzed for check-in spots by kernel density estimation (KDE) using ArcGIS. Furthermore, our results reveal that female users have a high rate of social media use, and significant difference is observed in check-in behavior during weekdays and weekends in the studied districts of Shanghai. Increase in check-ins is observed during the night as compared to the morning. From the results, it can be assumed that LBSN data can be helpful to observe gender difference.

[1]  Philip J. Reed,et al.  Observing gender dynamics and disparities with mobile phone metadata , 2016, ICTD.

[2]  Trevor Cohn,et al.  Mining user behaviours: a study of check-in patterns in location based social networks , 2013, WebSci.

[3]  Qingyun Du,et al.  Spatial and Social Media Data Analytics of Housing Prices in Shenzhen, China , 2016, PloS one.

[4]  Andrea Everard,et al.  Privacy Concerns Versus Desire for Interpersonal Awareness in Driving the Use of Self-Disclosure Technologies: The Case of Instant Messaging in Two Cultures , 2011, J. Manag. Inf. Syst..

[5]  S Law,et al.  Encounter and its configurational logic: Understanding spatiotemporal co-presence with road network and social media check-in data , 2017 .

[6]  F. Ren,et al.  Check-in behaviour and spatio-temporal vibrancy: An exploratory analysis in Shenzhen, China , 2018, Cities.

[7]  Li Li,et al.  Chinese Public Attention to the Outbreak of Ebola in West Africa: Evidence from the Online Big Data Platform , 2016, International journal of environmental research and public health.

[8]  Xiangliang Zhang,et al.  Minimizing User Involvement for Learning Human Mobility Patterns from Location Traces , 2016, AAAI.

[9]  Yan Zhang,et al.  Analysis of Attraction Features of Tourism Destinations in a Mega-City Based on Check-in Data Mining - A Case Study of Shenzhen, China , 2016, ISPRS Int. J. Geo Inf..

[10]  Xuhong Zhang,et al.  Activity correlation spectroscopy: a novel method for inferring social relationships from activity data , 2017, Social Network Analysis and Mining.

[11]  Michael Stefanone,et al.  Negotiating Social Belonging: Online, Offline, and In-Between , 2011, 2011 44th Hawaii International Conference on System Sciences.

[12]  Padhraic Smyth,et al.  Modeling human location data with mixtures of kernel densities , 2014, KDD.

[13]  Declan Traynor,et al.  Location-Based Social Networks , 2017, Encyclopedia of GIS.

[14]  Lee Humphreys,et al.  Mobile social networks and urban public space , 2010, New Media Soc..

[15]  Kun Yang,et al.  Mobile Social Networks: Architectures, Social Properties, and Key Research Challenges , 2013, IEEE Communications Surveys & Tutorials.

[16]  Cecilia Mascolo,et al.  An Empirical Study of Geographic User Activity Patterns in Foursquare , 2011, ICWSM.

[17]  A. Soliman,et al.  Social sensing of urban land use based on analysis of Twitter users’ mobility patterns , 2017, PloS one.

[18]  Stéphane Roche.  Geographic Information Science I , 2014 .

[19]  Patric R. Spence,et al.  Exploring extreme events on social media: A comparison of user reposting/retweeting behaviors on Twitter and Weibo , 2016, Comput. Hum. Behav..

[20]  Mohamed F. Mokbel,et al.  Recommendations in location-based social networks: a survey , 2015, GeoInformatica.

[21]  Nigel Waters,et al.  Using Social Media and Satellite Data for Damage Assessment in Urban Areas During Emergencies , 2017 .

[22]  Xing Xie,et al.  Learning location naming from user check-in histories , 2011, GIS.

[23]  Krzysztof Janowicz,et al.  What you are is when you are: the temporal dimension of feature types in location-based social networks , 2011, GIS.

[24]  Yu Liu,et al.  The promises of big data and small data for travel behavior (aka human mobility) analysis , 2016, Transportation research. Part C, Emerging technologies.

[25]  Vladlena Benson,et al.  Information disclosure of social media users: Does control over personal information, user awareness and security notices matter? , 2015, Inf. Technol. People.

[26]  Farid Karimipour,et al.  INTRA-URBAN MOVEMENT FLOW ESTIMATION USING LOCATION BASED SOCIAL NETWORKING DATA , 2015 .

[27]  Timothy Baldwin,et al.  Geolocation Prediction in Social Media Data by Finding Location Indicative Words , 2012, COLING.

[28]  Panagiotis Takis Metaxas,et al.  The power of prediction with social media , 2013, Internet Res..

[29]  Bo Wang,et al.  Delineation of an urban agglomeration boundary based on Sina Weibo microblog ‘check-in’ data: A case study of the Yangtze River Delta , 2017 .

[30]  José Ramón Gil-García,et al.  Government innovation through social media , 2013, Gov. Inf. Q..

[31]  Jianfa Shen,et al.  Shanghai: Urban Development and Regional Integration Through Mega Projects , 2017 .

[32]  Allan J. Brimicombe,et al.  Location-Based Services and Geo-Information Engineering , 2009 .

[33]  Lei Zheng,et al.  Innovation through social media in the public sector: Information and interactions , 2014, Gov. Inf. Q..

[34]  Krzysztof Janowicz,et al.  On the semantic annotation of places in location-based social networks , 2011, KDD.

[35]  Yannis Charalabidis,et al.  Participative Public Policy Making Through Multiple Social Media Platforms Utilization , 2012, Int. J. Electron. Gov. Res..

[36]  Dave Yates,et al.  Emergency knowledge management and social media technologies: A case study of the 2010 Haitian earthquake , 2010, ASIST.

[37]  Hsin-Yi Huang,et al.  Examining the beneficial effects of individual's self-disclosure on the social network site , 2016, Comput. Hum. Behav..

[38]  Naci Karkin,et al.  The use of twitter by mayors in Turkey: Tweets for better public services? , 2013, Gov. Inf. Q..

[39]  Andrea De Mauro,et al.  A formal definition of Big Data based on its essential features , 2016 .

[40]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[41]  Carlo Ratti,et al.  Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data , 2013, PloS one.

[42]  Kyumin Lee,et al.  Exploring Millions of Footprints in Location Sharing Services , 2011, ICWSM.

[43]  Nathan Eagle,et al.  Who's Calling? Demographics of Mobile Phone Use in Rwanda , 2010, AAAI Spring Symposium: Artificial Intelligence for Development.

[44]  Athena Vakali,et al.  Urban Planning and Smart Cities: Interrelations and Reciprocities , 2012, Future Internet Assembly.

[45]  Jiasheng Wang,et al.  Research on Evaluation of Popularity of Lijiang Scenic Area Based on Microblog Data , 2017 .

[46]  Xiaojun Shi,et al.  The threshold effect of high‐level human capital investment on China's urban‐rural income gap , 2011 .

[47]  Linli Cui,et al.  Urbanization and its environmental effects in Shanghai, China , 2012 .

[48]  Sejin Park,et al.  The role of social media in local government crisis communications , 2015 .

[49]  Xing Xie,et al.  Mining correlation between locations using human location history , 2009, GIS.

[50]  Jie Yin,et al.  Using Social Media to Enhance Emergency Situation Awareness , 2012, IEEE Intelligent Systems.

[51]  Cliff Lampe,et al.  The Benefits of Facebook "Friends: " Social Capital and College Students' Use of Online Social Network Sites , 2007, J. Comput. Mediat. Commun..

[52]  J. Lee,et al.  Using Social Media for Emergency Response and Urban Sustainability: A Case Study of the 2012 Beijing Rainstorm , 2015 .

[53]  Xiang Li,et al.  Explore Spatiotemporal and Demographic Characteristics of Human Mobility via Twitter: A Case Study of Chicago , 2015, ArXiv.

[54]  Ilyoung Hong.  Spatial Analysis of Location-Based Social Networks in Seoul, Korea , 2015 .

[55]  Nicholas Jing Yuan,et al.  Geo-social media data analytic for user modeling and location-based services , 2016, SIGSPACIAL.

[56]  Stefano Secci,et al.  Estimating human trajectories and hotspots through mobile phone data , 2014, Comput. Networks.

[57]  Dave Yates,et al.  Emergency knowledge management and social media technologies: A case study of the 2010 Haitian earthquake , 2011, Int. J. Inf. Manag..

[58]  M. Goodchild,et al.  Data-driven geography , 2014, GeoJournal.

[59]  Xing Xie,et al.  Mining Individual Life Pattern Based on Location History , 2009, 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware.

[60]  Guanling Chen,et al.  Sharing location in online social networks , 2010, IEEE Network.

[61]  Philip L. Roth,et al.  Social Media for Selection? Validity and Adverse Impact Potential of a Facebook-Based Assessment , 2016 .

[62]  Cecilia Mascolo,et al.  Socio-Spatial Properties of Online Location-Based Social Networks , 2011, ICWSM.

[63]  Jun Yan,et al.  Kernel Density Estimation of traffic accidents in a network space , 2008, Comput. Environ. Urban Syst..

[64]  Jianfa Shen,et al.  Development and Planning in Seven Major Coastal Cities in Southern and Eastern China , 2016 .

[65]  K. Saravanakumar,et al.  On Privacy and Security in Social Media – A Comprehensive Study☆ , 2016 .

[66]  Danah Boyd,et al.  Social Network Sites: Definition, History, and Scholarship , 2007, J. Comput. Mediat. Commun..

[67]  Eric Hsueh-Chan Lu,et al.  Personalized trip recommendation with multiple constraints by mining user check-in behaviors , 2012, SIGSPATIAL/GIS.

[68]  Eugenio Cesario,et al.  Trajectory Pattern Mining for Urban Computing in the Cloud , 2017, IEEE Transactions on Parallel and Distributed Systems.

[69]  Ke Zhang,et al.  Characterizing users’ check-in activities using their scores in a location-based social network , 2014, Multimedia Systems.

[70]  Yang Bai,et al.  Correlations between Socioeconomic Drivers and Indicators of Urban Expansion: Evidence from the Heavily Urbanised Shanghai Metropolitan Area, China , 2017 .

[71]  William T Riley,et al.  From Big Data to Knowledge in the Social Sciences , 2015, The Annals of the American Academy of Political and Social Science.

[72]  C. S. Andreassen.  Online Social Network Site Addiction: A Comprehensive Review , 2015, Current Addiction Reports.

[73]  Massimo Franco,et al.  Use of social media for e-Government in the public health sector: A systematic review of published studies , 2017, Gov. Inf. Q..

[74]  Deborah Agostino,et al.  Using social media to engage citizens: A study of Italian municipalities , 2013 .

[75]  Yeran Sun,et al.  Investigation of Travel and Activity Patterns Using Location-based Social Network Data: A Case Study of Active Mobile Social Media Users , 2015, ISPRS Int. J. Geo Inf..

[76]  Xuechen Xiong,et al.  Using the Fusion Proximal Area Method and Gravity Method to Identify Areas with Physician Shortages , 2016, PloS one.

[77]  Heather Richter Lipford,et al.  Examining privacy and disclosure in a social networking community , 2007, SOUPS '07.

[78]  Domenico Talia,et al.  Mining human mobility patterns from social geo-tagged data , 2016, Pervasive Mob. Comput..

[79]  Rocío Abascal-Mena,et al.  SOCIAL MEDIA PARTICIPATION IN URBAN PLANNING: A NEW WAY TO INTERACT AND TAKE DECISIONS , 2017 .

[80]  Saba Mahmood,et al.  Location based social media data analysis for observing check-in behavior and city rhythm in Shanghai , 2017, 4th International Conference on Smart and Sustainable City (ICSSC 2017).

[81]  Lars Backstrom,et al.  Find me if you can: improving geographical prediction with social and spatial proximity , 2010, WWW '10.

[82]  Celia Ross.  Regional China: A Business and Economic Handbook by Rongxing Guo , 2015 .

[83]  Xiaoqing Yu,et al.  Explore Hot Spots of City Based on DBSCAN Algorithm , 2014, 2014 International Conference on Audio, Language and Image Processing.

[84]  Huan Liu,et al.  Mining Human Mobility in Location-Based Social Networks , 2015, Mining Human Mobility in Location-Based Social Networks.

[85]  Feng Xia,et al.  Event-Based Mobile Social Networks: Services, Technologies, and Applications , 2014, IEEE Access.

[86]  Xiaokun Gu,et al.  Spatial accessibility of country parks in Shanghai, China , 2017 .