Understanding the User Behavior of Foursquare: A Data-Driven Study on a Global Scale

Abstract

Being a leading online service providing both local search and social networking functions, Foursquare has attracted tens of millions of users all over the world. Understanding the user behavior of Foursquare is helpful to gain insights for location-based social networks (LBSNs). Most of the existing studies focus on a biased subset of users, which cannot give a representative view of the global user base. Meanwhile, although the user-generated content (UGC) is very important to reflect the user behavior, most of the existing UGC studies of Foursquare are based on the check-ins. There is a lack of a thorough study on tips, the primary type of UGC on Foursquare. In this paper, by crawling and analyzing the global social graph and all published tips, we conduct the first comprehensive user behavior study of all 60+ million Foursquare users around the world. We have made the following three main contributions. First, we have found a number of unique and undiscovered features of the Foursquare social graph on a global scale, including a moderate level of reciprocity, a small average clustering coefficient, a giant strongly connected components, and a significant community structure. Besides the singletons, most of the Foursquare users are weakly connected with each other. Second, we undertake a thorough investigation according to all published tips on Foursquare. We start from counting the numbers of tips published by different users, then look into the tip contents from the perspectives of tip venues, temporal patterns and sentiment. Our results provide an informative picture of the tip publishing patterns of Foursquare users. Last but not least, as a practical scenario to help third-party application providers, we propose a supervised machine learning-based approach to predict whether a user is an influential by referring to her profile and UGC, instead of relying on the social connectivity information. Our data-driven evaluation demonstrates that our approach can reach a good prediction performance with an F1-score of 0.87 and an AUC value of 0.88. Our findings provide a systematic view of the behavior of Foursquare users, and are constructive for different relevant entities, including LBSN service providers, Internet service providers and third-party application providers.

Complementing Location-Based Social Network Data With Mobility Data: A Pattern-Based Approach

“…To complement Foursquare check-in data, the authors [26] propose to analyze the published tips trips venues and temporal patterns. Since they consist of User-Generated Content, the provided information is substantially different from those provided by mobility data.…”

Characterizing and Understanding Development of Social Computing Through DBLP: A Data-Driven Analysis

“…As in Refs. [25,26], we use Google Map APIs to identify those extractions failed by Geotext. In detail, we use Google Map APIs to convert location strings to geographic coordinates, and then use Google Map APIs to obtain the country information from geographic coordinates.…”

Cross-site Prediction on Social Influence for Cold-start Users in Online Social Networks

“…Many of the emerging OSNs, such as Foursquare [7]/Swarm [8,51] and Medium, took advantage of their users' accounts on dominant OSNs to enhance their function-orientated services. They supported a cross-site linking function [18], allowing users to link their accounts on dominant OSNs, e.g., Twitter and Facebook.…”

“…Although dominant OSNs serve billions of users, there are still a number of emerging OSNs, normally with special focuses. For example, Foursquare [7] focuses on location-centric activities, and Medium (https://www.medium.com/) offers blog services. New emerging OSNs are launched from time to time, whose users usually have relatively smaller ages 1 .…”