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Bridging the Gap Between Physical Location and Online Social Networks

Bridging the Gap Between Physical Location and Online Social Networks

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    91.650 Paper Presentation 91.650 Paper Presentation Presentation Transcript

    • Bridging the Gap Between Physical Location and Online Social Networks J. Cranshaw, E. Toch, J. I. Hong, A. Kittur, and N. Sadeh. In Proceedings of the 12th ACM International Conference on Ubiquitous Computing, Copenhagen, Denmark, September 2010. Presented by Beibei Yang UMass Lowell 91.650, Spring 2011Tuesday, April 12, 2011 1
    • Overview • Examines the location traces of 489 users • Introduces location-based features for analyzing geographic regions ‣ location entropy • Provide model for predicting friends • Identify relationships between users’ mobility patterns and structural properties of their underlying social network • Potential design and research of online social networks on offline mobilityTuesday, April 12, 2011 2
    • Motivation • Difficult distinction of online and offline social networks • Open ended debate: ‣ “online social networks are contributing to the isolation of people in the physical world”--Deresiewicz ‣ “online social networks have a positive impact on social relations in the physical world”--Pew Internet and American Life • Distinction further blurred by ubiquity of location- enabled smartphonesTuesday, April 12, 2011 3
    • Motivation • Difficult distinction of online and offline social networks • Open ended debate: ‣ “online social networks are contributing to the isolation of people in the physical world”--Deresiewicz ‣ “online social networks have a positive impact on social relations in the physical world”--Pew Internet and American Life • Distinction further blurred by ubiquity of location- enabled smartphonesTuesday, April 12, 2011 3
    • Motivation • Difficult distinction of online and offline social networks • Open ended debate: ‣ “online social networks are contributing to the isolation of people in the physical world”--Deresiewicz ‣ “online social networks have a positive impact on social relations in the physical world”--Pew Internet and American Life • Distinction further blurred by ubiquity of location- enabled smartphonesTuesday, April 12, 2011 3
    • Challenges • Infer properties of user social behaviors from their location trails. - Measure user similarity based on mobility to infer user social structures [Eagle et al. (2009) and Li et al. (2008)] - Co-location of two users insufficient to determine their relationship, especially in urban areas, where co-location among strangers is frequent. [Miklas et al. (2007)] • In reality, location tracking is inherently partial and inexact.Tuesday, April 12, 2011 4
    • Contribution • Evaluate on two main tasks - Predicting whether two co-located users are friends on Facebook - Predicting number of friends a user has • Contributions: 1. Establish model of friendship by co-location 2. Find relationship between mobility pattern and number of friends 3. Show diversity of location can be used to analyze the context of social interactionsTuesday, April 12, 2011 5
    • Related Work • Statistical modeling of mobility patterns - Examined features of mobility - Tracked phone conversations - Number of unique locations - Proximity at work, Saturday night, etc. - Self report of important factors • Most work relied solely on co-location without digging furtherTuesday, April 12, 2011 6
    • MethodologyTuesday, April 12, 2011 7
    • Locaccino • Web-application for Facebook • Developed by Mobile Commerce Lab at CMU • Allows users to share location ‣ Facebook controlled privacy rules • Contains two components • Web Application ‣ Query friends’ locations ‣ Review Privacy rules • Locator Software ‣ Updates user location http://locaccino.org/ ‣ Run on laptops and mobile phones ‣ Update locations every 10 minutesTuesday, April 12, 2011 8
    • Locaccino • Locator software uses combination of: - GPS if applicable (Accurate ~10m-15m) - WiFi lookup service (Accurate ~10m-20m) - IP geolocation (city or neighborhood level granularity) • Sends time, latitude and longitude to LocaccinoTuesday, April 12, 2011 9
    • User Demographics • 489 users of Locaccino • Ranging from 7 days to several months (Average 74 days, median of 38 days) • Use at different times and for different reasons • Mostly from university campusTuesday, April 12, 2011 10
    • Data Collection • 3 million location observations - 2 million in Pittsburgh - ignore IP geolocation - 93.7% from laptop locator software • Divide lat. and lon. into 30m x 30m grid • Use 10 min. interval for time coordinate • Co-location = same grid + same timeTuesday, April 12, 2011 11
    • Network Data • Social Network (S) – Friends in Facebook • Co-location Network (C) – Co-located at least once • Co-located Friends Network (S ∩ C) – Friends and co- locatedTuesday, April 12, 2011 12
    • Location Diversity Measurement • Frequency – Raw count of observations • User Count – Total unique visitors • Entropy – Number of users and proportions of their observationsTuesday, April 12, 2011 13
    • Co-location Features • Intensity and Duration – Size and spatial and temporal range. How long and how actively users have embraced the system. • Location Diversity – Frequency, user count and entropy • Specificity – How specific a location is to a given co-location [TFIDF (l)] u1,u2 • Structural Properties – Measures the strength of the relationship between two co-located usersTuesday, April 12, 2011 14
    • Other Measured Features • Regularity of a user’s routine: {L, D, H} • User mobility features - Intensity and duration - Location diversity: Location observations of a single user - Mobility regularityTuesday, April 12, 2011 15
    • Results • 6 classifiers, 50-fold cross validation • Performance: • AdaBoost > Random Forest > SVM • Overall accuracy of AdaBoost: 92% - Guess better on non-friendship than friendshipTuesday, April 12, 2011 16
    • y n sit e t int o u ith W l e od m Full Number of co- locations! ! tu res ea yf sit en IntTuesday, April 12, 2011 17
    • Inferring Number of Friends • Look to relate number of Facebook friends to mobility patterns • Expectations: - Users who have used the system longer have more friends - Users who visit “high diversity” locations have more friends - Users with irregular schedules may have more friends (require help from Locaccino)Tuesday, April 12, 2011 18
    • Pearson Correlation of User Mobility Features • Worst: Intensity and duration • Best: Location diversity • MaxEntropy, MaxUserCount, MaxFreq bestTuesday, April 12, 2011 19
    • Conclusions • Co-location network 3x larger than social network (edge-wise) - Social network better connected • Properties of location are crucial - Especially Entropy - Difference between high and low entropy - Help define both relationships and number of friends • Created set of features to help classify social network friends - Better than by simple co-location observations • Found interesting patterns - Co-location without friends - Friends without co-locationTuesday, April 12, 2011 20
    • Future Work • Use classifiers for social network friend recommendation system • Augment and expand current friend-link system in place • Could help provide insight into strength of relationship • Still requires more research and validation • Develop system for segregating and categorizing friends • Help with privacy rules • Build off relationship between online and offline social behavior • Using things such as entropy of a location • Use of location patterns of users • Suggest similar locations to friends • Suggest similar locations to non-friends with similar behaviorTuesday, April 12, 2011 21
    • AppendixTuesday, April 12, 2011 22
    • Tuesday, April 12, 2011 23