91.650 Paper Presentation

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 YangUMass Lowell 91.650, Spring 2011

1Tuesday, April 12, 2011

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 mobility


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 smartphones


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.


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 interactions


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 further


Methodology


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

‣ Run on laptops and mobile phones

‣ Update locations every 10 minutes

http://locaccino.org/


http://locaccino.org

http://locaccino.org

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 Locaccino


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 campus


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 time


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-located


Location Diversity Measurement• Frequency – Raw count of observations

• User Count – Total unique visitors

• Entropy – Number of users and proportions of their observations


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 [TFIDFu1,u2(l)]

• Structural Properties – Measures the strength of the relationship between two co-located users


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 regularity


Results• 6 classifiers, 50-fold cross validation

• Performance:

• AdaBoost > Random Forest > SVM

• Overall accuracy of AdaBoost: 92%

- Guess better on non-friendship than friendship


Inte

nsity

feat

ures!

Num

ber

of co-

locatio

ns!

Without in

tensity

Full model


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)


Pearson Correlation of User Mobility Features

• Worst: Intensity and duration

• Best: Location diversity

• MaxEntropy, MaxUserCount, MaxFreq best


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-location


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 behavior


Appendix


91.650 Paper Presentation

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