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Acquaintance or Partner? Predicting Partnership in Online and Location-Based Social Networks
 

Acquaintance or Partner? Predicting Partnership in Online and Location-Based Social Networks

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Existing approaches to predict tie strength between users cover either online social networks or location-based social networks. However, there are no studies that combine these networks to unveil ...

Existing approaches to predict tie strength between users cover either online social networks or location-based social networks. However, there are no studies that combine these networks to unveil information about the intensity of the social relations between users. In this research paper we analyze aspects of tie strength – defined as partners and acquaintances – from two different domains: a location-based social network and an online social network for residents of Second Life. We compare user pairs according to their partnership and reveal significant differences between partners and acquaintances. Following these observations, we evaluate the social proximity of users with supervised and unsupervised learning algorithms and identified homophilic features as most valuable for the prediction of partnership.

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    Acquaintance or Partner? Predicting Partnership in Online and Location-Based Social Networks Acquaintance or Partner? Predicting Partnership in Online and Location-Based Social Networks Presentation Transcript

    • Information Systems and Computer Media - Graz University of Technology! Acquaintance or Partner? ! ! Predicting Partnership in Online and ! Location-Based Social Networks! Michael Steurer and Christoph Trattner! Graz University of Technology! Austria! Predicting Partnership in Online and Location-Based Social Networks!
    • Information Systems and Computer Media - Graz University of Technology! Introduction! !   Partner vs. Acquaintance! !   !   Tie strength! Combined Data from Second Life! !   Online social network! Location-based social network! !   Compute Features! !   !   Experiments! !   !   Compare differences! Predictability of partnership! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Second Life! http://notizen.typepad.com/aus_der_provinz/sl061018_001_1.jpg! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Event Data! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Position Data! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Location-Based Data! !   Event Data! !   !   !   12 months starting in March 2012! 262,234 unique events ! Location-based Social Data! !   !   !   4,105 unique regions! 19 Million data samples! 410,619 unique users ! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Online Social Data! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Online Social Data! !   Online Social Data! !   152,509 unique users! 1,084,002 Postings (text messages, snapshots)! !   459,734 Comments! !   1,631,568 Loves! !   !   Groups and Interests! !   285,528 unique groups! 15.51 groups per user on average! !   6.5 interests per user on average! !   Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Partnership ! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Partnership ! !   Partnership! !   ! 39,468 users (out of 152,509)! Compare to Real Life! ! Marriage – 10 Linden Dollars! ! Divorce – 25 Linden Dollars! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Networks! !   Created Networks! !   !   !   !   Location-based social network! Intersection! Topological Features! !   ! Online social network! Common Neighbors, Adamic-Adar, …! Homophilic Features! !   Groups, regions, distance, …! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Experimental Setup! !   Merged Networks + Features! !   !   !   Users available in both networks! 44,603 users and 1,419,543 edges! 3,096 users in partnership! ! !   Partner or Acquaintances! !   !   Differences! Predictability! ! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Partner vs. Acquaintance ! !   Compare User-Pairs! !   !   !   In a relationship! With interaction in online social network! Measures and Analysis! !   !   Mean values ! Significance tests! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Online Social Network! ***p<0.001! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Location-based Social Network! ***p<0.001! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Predict Partnership ! !   Balanced Dataset 3,000 User-Pairs! !   !   !   1,500 partners and 1,500 acquaintances! Random guessing 50%! Area Under the ROC Curve (AUC)! !   Logistic Regression, SVM, Random Forest! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Supervised Learning ! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Feature Analysis ! !   Information Gain and AUC! Collaborative Filtering! !   Online Social Network! !   !   !   Topological Features! Location Based Social Network! ! Homophilic Features! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Feature Analysis ! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Conclusion! !   Large Dataset of Different Domains! !   !   !   Online Social and Location-Based! Same Users! Computed ‘Proximity’ Between Users! !   ! !   !   Topological! Homophilic! Significant Differences! Predict Partnership with 93.3%! Predicting Partnership in Online and Location-Based Social Networks! Michael Steurer! !
    • Information Systems and Computer Media - Graz University of Technology! Acquaintance or Partner? ! ! Predicting Partnership in Online and ! Location-Based Social Networks! Michael Steurer and Christoph Trattner! Graz University of Technology! Austria! Predicting Partnership in Online and Location-Based Social Networks!