Exploring Millions of Footprints in Location Sharing Services

By Zhiyuan Cheng, James Caverlee, Kyumin Lee, Daniel Z. Sui. International Conference on Web and Social Media, 2011

762 citations recorded by the source corpus.

Location sharing services (LSS) like Foursquare, Gowalla, and Facebook Places support hundreds of millions of user-driven footprints (i.e., "checkins"). Those global-scale footprints provide a unique opportunity to study the social and temporal characteristics of how people use these services and to model patterns of human mobility, which are significant factors for the design of future mobile+location-based services, traffic forecasting, urban planning, as well as epidemiological models of disease spread. In this paper, we investigate 22 million checkins across 220,000 users and report a quantitative assessment of human mobility patterns by analyzing the spatial, temporal, social, and textual aspects associated with these footprints. We find that: (i) LSS users follow the “Levy Flight” mobility pattern and adopt periodic behaviors; (ii) While geographic and economic constraints affect mobility patterns, so does individual social status; and (iii) Content and sentiment-based analysis of posts associated with checkins can provide a rich source of context for better understanding how users engage with these services.

Subfields: Computer Science Applications, Marketing, Transportation

View Paper: https://www.semanticscholar.org/paper/36f732b800f2e91c435cb9072786f6be8f85884c

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Citations in the corpus

The Geography and Importance of Localness in Geotagged Social Media (2016, background): “For instance, this has occurred in work that seeks to describe diurnal patterns in sentiment [15], predict public health measures [9], and measure human mobility [5], among other studies (e.”
Where You Like to Go Next: Successive Point-of-Interest Recommendation (2013, positive): “…social networks have received much attention in recent years due to the new characteristics of spatial-temporal-social information embedded in the check-in data and the prevalence of various interesting real-world applications [Yuan et al. , 2012; Zheng et al. , 2011; Cheng et al. , 2011a].”
Learning Graph-based POI Embedding for Location-based Recommendation (2016, methodology): “2010 to Jan 2011 through Twitter with the same crawling strategy as proposed in [5].”
Recommendations in location-based social networks: a survey (2015, background): “Twitter [18] This dataset4 contains 22 million geo-tagged tweets collected via Twitter APIs.”
Event-based social networks: linking the online and offline social worlds (2012, neutral): “6(a), we examine the distance of a Meetup event location and a Gowalla checkin location to the user’s home location [4, 5].”
Exploring temporal effects for location recommendation on location-based social networks (2013, background): “[4] investigated the “Lèvy Flight” property of human check-in patterns, and discovered that social status is affected by geographic constraints.”
OMG, I Have to Tweet that! A Study of Factors that Influence Tweet Rates (2012, background): “Similar analyses are now being made of locationbased social networks—also reliant on self-reporting of location via check-ins—to better understand patterns of human mobility for traffic forecasting, urban planning and other applications (Cheng et al. 2011 ).”
PrivCheck: privacy-preserving check-in data publishing for personalized location based services (2016, background): “In this paper, due to the limited availability of user proﬁle data in the collected dataset, we deﬁne two attributes as private, i.e, gender (male/female) and social status [5] (a yardstick to measure the popularity of a user in social network).”
Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning (2017, background): “The GTSM data provide an unprecedented coverage of today’s major cities [22] and serve as a detailed proxy for understanding human activities [17, 5, 27, 18, 8, 26].”
Understanding intra-urban trip patterns from taxi trajectory data (2012, background): “2006), travel bugs (Brockmann and Theis 2008), and check-ins in location sharing services (Cheng et al. 2011) can also be used for understanding human mobility patterns.”
Mining User Mobility Features for Next Place Prediction in Location-Based Services (2012, background): “Even though location-based services have only recently enjoyed mainstream popularity, they have already attracted research efforts thanks to the new wealth of social and spatial data they offer [1].”
Geo-located Twitter as proxy for global mobility patterns (2013, methodology): “59) and Foursquare check-ins (Cheng et al. 2011), β = 1.”
Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks (2016, methodology): “27%, and recursively searching disjoint grid cells [Cheng et al. 2011], which results in an accuracy of 83.”
Beyond "local", "categories" and "friends": clustering foursquare users with latent "topics" (2012, background): “Data drawn from mobile devices on the whereabouts of their users has led to an influx of interesting findings in explaining patterns of human mobility [6, 17, 5], predicting friendship on social networking sites based on location data [23, 20], and better understanding different aspects of cities, b”
Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction (2014, methodology): “With users’ review data, we estimate a user’s ‘home location’ by using the recursive grid search algorithm proposed in [5].”
Representation and communication: challenges in interpreting large social media datasets (2013, methodology): “More specifically, location-based networks have provided researchers with large data sets of location data that they use for creating models of prediction in terms of issues such as friend connections and human mobility [8, 11, 24], check-ins have for example been used to identify protoneighbourhood”
Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr (2013, methodology): “Furthermore, check-ins collected from location-sharing services were used to study human mobility patterns (Cheng et al. 2011).”
Social Media data: Challenges, opportunities and limitations in urban studies (2019, background): “…data retrieved from Location Based Social Networks —LBSNs hereafter— provide a means by which to assess different urban dynamics, such as, mobility (Cheng, Caverlee, Lee, & Sui, 2011; Luo, Cao, Mulligan, & Li, 2016; Noulas, Scellato, Lambiotte, Pontil, & Mascolo, 2012; Quercia, Aiello,…”