Predicting Social Interactions from Different Sources of Location-based Knowledge
Recent research has shown that digital online geo- location traces are new and valuable sources to predict social interactions between users, e.g. , check-ins via FourSquare or geo-location information in Flickr images. Interestingly, if we look at related work in this area, research studying the extent to which social interactions can be predicted between users by taking more than one location-based knowledge source into account does not exist. To contribute to this field of research, we have collected social interaction data of users in an online social network called My Second Life and three related location-based knowledge sources of these users (monitored locations, shared locations and favored locations), to show the extent to which social interactions between users can be predicted. Using supervised and unsupervised machine learning techniques, we find that on the one hand the same location-based features (e.g. the common regions and common observations) perform well across the three different sources. On the other hand, we find that the shared location information is better suited to predict social interactions between users than monitored or favored location information of the user.
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Predicting Social Interactions from Different Sources of Location-based Knowledge
- 1. Predicting Social Interactions Based on Different Sources of Location-based Knowledge Michael Steurer - msteurer@iicm.edu, Graz University of Technology Christoph Trattner - ctrattner@know-center.at, Graz University of Technology Denis Helic - dhelic@tugraz.at, Graz University of Technology
- 2. What Did We Do? Predict Interactions in Social Network Postings, Comments, Loves Similar to Facebook, G+ Three Different Sources of Location Data Monitored Locations Shared Locations Favoured Locations Predciting Social Interactions Using Location-Based Sources 2/23
- 3. Second Life source: http://notizen.typepad.com/aus_der_provinz/sl061018_001_1.jpg Predciting Social Interactions Using Location-Based Sources 3/23
- 5. Online Social Network Predciting Social Interactions Using Location-Based Sources 5/23
- 6. Collected 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 Predciting Social Interactions Using Location-Based Sources 6/23
- 8. 1) Monitored Locations Predciting Social Interactions Using Location-Based Sources 8/23
- 9. Collected Data Event Data 12 Months Starting in March 2012 Working Hours 24/7 Location-based Social Data 4,105 Unique Locations 19 Million Data Samples 410,619 Unique Users Predciting Social Interactions Using Location-Based Sources 9/23
- 10. 2) Shared Locations Predciting Social Interactions Using Location-Based Sources 10/23
- 11. Collected Data Compare to Check-ins, e.g. FourSquare Shared from "In-World" Harvested Locations 45,835 User Profiles 496,912 Snapshots 13,583 Unique Locations Predciting Social Interactions Using Location-Based Sources 11/23
- 12. 3) Favoured Locations Predciting Social Interactions Using Location-Based Sources 12/23
- 13. Collected Data Extracted from Profiles Limitations 10 per User Enhanced with Picture and Text Favoured Locations 191,610 User Profiles 811,386 Picks 25,311 Unique Locations Predciting Social Interactions Using Location-Based Sources 13/23
- 14. Predict Text-Interactions Use Location Information
- 15. Network Setup Create Networks from Data Online Social Network Enhance with Location-based Data Predciting Social Interactions Using Location-Based Sources 15/23
- 16. Feature Modeling Compute User Relation Common and Total Locations (Jaccard's Coefficient) Entropy Common Locations User Count Common Locations Frequency Common Locations Predciting Social Interactions Using Location-Based Sources 16/23
- 17. Experiment Setup Feature Modeling Unsupervised Learning with Ranked Lists Information Gain for Single Features Prediction Task Binary Classification Problem Supervised Learning Algorithms Logistic Regression, Random Forest, SVM Predciting Social Interactions Using Location-Based Sources 17/23
- 18. Results
- 19. Analysis of Homophily (*p < 0.1, **p < 0.01, and ***p < 0.001) Predciting Social Interactions Using Location-Based Sources 19/23
- 20. Predict Interactions Predciting Social Interactions Using Location-Based Sources 20/23
- 21. Conclusions
- 22. Conclusions User-Pairs with Interactions More Common and Total Locations Locations have Less Entropy, Frequency, and User-Count Results of the Prediction Common Locations, Jaccard Shared > Monitored > Favoured Same Characteristics Among Algorithms Logistic Regression was Best Predciting Social Interactions Using Location-Based Sources 22/23
- 23. Predicting Social Interactions Based on Different Sources of Location-based Knowledge Michael Steurer - msteurer@iicm.edu, Graz University of Technology Christoph Trattner - ctrattner@know-center.at, Graz University of Technology Denis Helic - dhelic@tugraz.at, Graz University of Technology