Not All Trips are Equal

Abstract

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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 5 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.