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Page 1: Predicting Social Interactions from Different Sources of Location-based Knowledge

Predicting Social InteractionsBased on Different Sources of Location-based Knowledge

Michael Steurer - [email protected], Graz University of TechnologyChristoph Trattner - [email protected], Graz University of TechnologyDenis Helic - [email protected], Graz University of Technology

Page 2: Predicting Social Interactions from Different Sources of Location-based Knowledge

What Did We Do?

Predciting Social Interactions Using Location-Based Sources

Predict Interactions in Social Network

Three Different Sources of Location Data

Postings, Comments, Loves

Similar to Facebook, G+

Monitored Locations

Shared Locations

Favoured Locations

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Page 3: Predicting Social Interactions from Different Sources of Location-based Knowledge

Second Life

Predciting Social Interactions Using Location-Based Sources

source: http://notizen.typepad.com/aus_der_provinz/sl061018_001_1.jpg

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Online Social NetworkText-Interaction Data

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Online Social Network

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Collected Data

Predciting Social Interactions Using Location-Based Sources

Online Social Data

Groups and Interests

152,509 Unique Users

1,084,002 Postings (Text Messages, Snapshots)

459,734 Comments

1,631,568 Loves

285,528 Unique Groups

15.51 Groups per User on Average

6.5 Interests per User on Average

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Page 7: Predicting Social Interactions from Different Sources of Location-based Knowledge

Location-based InformationUsers Position Data

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1) Monitored Locations

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Collected Data

Predciting Social Interactions Using Location-Based Sources

Event Data

Location-based Social Data

12 Months Starting in March 2012

Working Hours 24/7

4,105 Unique Locations

19 Million Data Samples

410,619 Unique Users

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2) Shared Locations

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Collected Data

Predciting Social Interactions Using Location-Based Sources

Compare to Check-ins, e.g. FourSquare

Shared from "In-World"

Harvested Locations

45,835 User Profiles

496,912 Snapshots

13,583 Unique Locations

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3) Favoured Locations

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Collected Data

Predciting Social Interactions Using Location-Based Sources

Extracted from Profiles

Limitations

Favoured Locations

10 per User

Enhanced with Picture and Text

191,610 User Profiles

811,386 Picks

25,311 Unique Locations

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Predict Text-InteractionsUse Location Information

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Network Setup

Predciting Social Interactions Using Location-Based Sources

Create Networks from Data

Online Social Network

Enhance with Location-based Data

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Page 16: Predicting Social Interactions from Different Sources of Location-based Knowledge

Feature Modeling

Predciting Social Interactions Using Location-Based Sources

Compute User Relation

Common and Total Locations (Jaccard's Coefficient)

Entropy Common Locations

User Count Common Locations

Frequency Common Locations

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Page 17: Predicting Social Interactions from Different Sources of Location-based Knowledge

Experiment Setup

Predciting Social Interactions Using Location-Based Sources

Feature Modeling

Prediction Task

Unsupervised Learning with Ranked Lists

Information Gain for Single Features

Binary Classification Problem

Supervised Learning Algorithms

Logistic Regression, Random Forest, SVM

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Page 18: Predicting Social Interactions from Different Sources of Location-based Knowledge

Results

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Analysis of Homophily

Predciting Social Interactions Using Location-Based Sources

(*p < 0.1, **p < 0.01, and ***p < 0.001)

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Predict Interactions

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Conclusions

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Conclusions

Predciting Social Interactions Using Location-Based Sources

User-Pairs with Interactions

Results of the Prediction

More Common and Total Locations

Locations have Less Entropy, Frequency, and User-Count

Common Locations, Jaccard

Shared > Monitored > Favoured

Same Characteristics Among Algorithms

Logistic Regression was Best

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Page 23: Predicting Social Interactions from Different Sources of Location-based Knowledge

Predicting Social InteractionsBased on Different Sources of Location-based Knowledge

Michael Steurer - [email protected], Graz University of TechnologyChristoph Trattner - [email protected], Graz University of TechnologyDenis Helic - [email protected], Graz University of Technology


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