Workdiamond2021

The Where and When of Finding New Friends: Analysis of a Location-based Social Discovery Network

Terence Chen · Mohamed Ali Kâafar · Roksana Boreli

33 citations0/0 files storedProceedings of the International AAAI Conference on Web and Social Media

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Human Mobility and Location-Based Analysis Privacy-Preserving Technologies in Data Privacy, Security, and Data Protection

Abstract

With more people accessing Online Social Networks (OSN) using their mobile devices, location-based features have become an important part of the social networking. In this paper, we present the first measurement study of a new category of location-based online social networking services, a location-based social discovery (LBSD) network, that enables users to discover and communicate with nearby people. Unlike popular check-in-based social networks, LBSD allows users to publicly reveal their locations without being associ- ated to a specific “venue” and their usage is not influenced by the incentive mechanisms of the underlying virtual community. By analyzing over 8 million user profiles and around 150 million location updates collected from a popular new LBSD network, we first present the characteristics of spatial- temporal usage patterns of the observed users, showing that 40% of updates are from the user’s primary location and 80% are from their top 10 locations. We identify events that trigger bursts of growth in subscriber numbers, showing the importance of social media marketing. Finally, we investigate how usage patterns may be utilized to re-identify individuals with e.g. different identifiers or from datasets belonging to different online services. We evaluate re-identification by usage, spatial and spatial-temporal patterns and using a number of metrics and show that the best results can be achieved using location data, with a high accuracy: our experiments demonstrate that we can re-identify up-to 85% of users with a precision of 77% using monitored spatial data. Overall, we find that although users exhibit strong periodic behavior in their usage pattern and movements, the success rate of re-identification is highly dependent on the level of activeness and the lifetime of the users in the network.

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References

2011Friendship and mobility 2021Exploring Millions of Footprints in Location Sharing Services 2010Inferring social ties from geographic coincidences 2010Bridging the gap between physical location and online social networks 2009Analysis of a Location-Based Social Network

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2020User Mobility Synthesis based on Generative Adversarial Networks: A Survey 2016A deep dive into location-based communities in social discovery networks 2017Smart cities, urban sensing, and big data: mining geo-location in social networks 2017Who uses mobile apps to meet strangers: The roles of core traits and surface characteristics 2013Social streams based on network proximity

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