Not all trips are equal: Analyzing foursquare check-ins of trips and city visitors
Location-Based Social Networks (LBSN) such as Foursquare allow users to indicate venue visits via check-ins. This results in much fine grained context-rich data, useful for studying user mobility. In this work, we use check-ins to characterize trips and visitors to two cities, where visitors are def...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2015
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/3104 https://ink.library.smu.edu.sg/context/sis_research/article/4104/viewcontent/Not_All_Trips_Equal_pv_oa.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Location-Based Social Networks (LBSN) such as Foursquare allow users to indicate venue visits via check-ins. This results in much fine grained context-rich data, useful for studying user mobility. In this work, we use check-ins to characterize trips and visitors to two cities, where visitors are defined as having their home cities elsewhere. First, we divide trips into two duration types: long and short. We then show that trip types differ in check-in distributions over venue categories, time slots, as well as check-in intensity. Based on the trip types, we then divide visitors into long-term and short-term visitors. We compare visitor types in terms of popularities of check-in venues and proximities to friends' check-ins. Our findings indicate that short-term visitors are more biased towards popular venues. As for proximity to friends' check-ins, the effect is more consistently observed for long-term visitors. These findings also illustrate that locations of incoming visitors can effectively be analyzed using LBSN data in addition to conducting user surveys which are relatively costlier. Lastly, we investigate the importance of visitor type information in models for venue prediction. We apply models including a state of the art kernel density estimation technique and ranking based on venue popularity. For each model, we consider two settings where visitor type information is absent/present. For long-term visitors, we observed little differences in accuracies. However, for short-term visitors, predictions are significantly more accurate by using type information. These findings suggest that venue prediction or recommender systems should consider visitor type to improve accuracy. |
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