Location-Based Social Networks

Showing results in 'Publications'. Show all posts
Preoţiuc-Pietro, Daniel, Justin Cranshaw, and Tae Yano. Exploring venue-based city-to-city similarity measures In Workshop on Urban Computing (UrbComp). SIGKDD., 2013. AbstractPDF

In this work we explore the use of incidentally generated social network data for the folksonomic characterization of cities by the types of amenities located within them. Using data collected about venue categories in various cities, we examine the effect of different granularities of spatial aggregation and data normalization when representing a city as a collection of its venues. We introduce three vector-based representations of a city, where aggregations of the venue categories are done within a grid structure, within the city’s municipal neighborhoods, and across the city as a whole. We apply our methods to a novel dataset consisting of Foursquare venue data from 17 cities across the United States, totaling over 1 million venues. Our preliminary investigation demonstrates that different assumptions in the urban perception could lead to qualitative, yet distinctive, variations in the induced city description and categorization.

Preoţiuc-Pietro, Daniel, and Trevor Cohn. Mining user behaviours: A study of check-in patterns in Location Based Social Networks. WebSci., 2013. AbstractPDFPoster

Understanding the patterns underlying human mobility is of an essential importance to applications like recommender systems. In this paper we investigate the behaviour of around 10,000 frequent users of Location Based Social Networks (LBSNs) making use of their full movement patterns. We analyse the metadata associated with the whereabouts of the users, with emphasis on the type of places and their evolution over time. We uncover patterns across different temporal scales for venue category usage. Then, focusing on individual users, we apply this knowledge in two tasks: 1) clustering users based on their behaviour and 2) predicting users’ future movements. By this, we demonstrate both qualitatively and quantitatively that incorporating temporal regularities is beneficial for making better sense of user behaviour.