Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Quality control for real-time ubiquitous crowdsourcing
Proceedings of the 2nd international workshop on Ubiquitous crowdsouring
Do all birds tweet the same?: characterizing twitter around the world
Proceedings of the 20th ACM international conference on Information and knowledge management
Social disclosure of place: from location technology to communication practices
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Inferring land use from mobile phone activity
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Exploiting large-scale check-in data to recommend time-sensitive routes
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Discovering urban spatial-temporal structure from human activity patterns
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Exploring trajectory-driven local geographic topics in foursquare
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Characterizing Urban Landscapes Using Geolocated Tweets
SOCIALCOM-PASSAT '12 Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
Visualizing the Invisible Image of Cities
GREENCOM '12 Proceedings of the 2012 IEEE International Conference on Green Computing and Communications
DCOSS '13 Proceedings of the 2013 IEEE International Conference on Distributed Computing in Sensor Systems
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Social media systems allow a user connected to the Internet to provide useful data about the context in which they are at any given moment, such as Instagram and Foursquare, which are called participatory sensing systems. Location sharing services are examples of participatory sensing systems. The sensed data is a check-in of a particular place that indicates, for instance, a restaurant in a specific location, and also a signal from a user expressing his/her preference. From a participatory sensing system we can derive a participatory sensor network. In this work we compare two different participatory sensor networks, one derived from Instagram, and another one derived from Foursquare. In Instagram, the sensed data is a picture of a specific place. On the other hand, in Foursquare the sensed data is the actual location associated with a specific category of place (e.g., restaurant). Using those social networks we can extract information in many ways. In this work we are interested in comparing two datasets of Foursquare and two datasets of Instagram. We analyze those datasets to investigate whether we can observe the same users' movement pattern, the popularity of regions in cities, the activities of users who use those social networks, and how users share their content along the time. In answering those questions, we want to better understand location-related information, which is an important aspect of the urban phenomena.