M odeling and Predicting Personal Information Dissemination Behavior

1. Modeling and Predicting Personal Information Dissemination Behavior Authors: Ching-Yung Lin Belle L. Tseng Ming-Ting Sun Speaker: Yi-Ching Huang

2. Outline • Introduction • CommuntiyNet • Community Analysis • Individual Analysis • CommunityNet Applications • Conclusions

3. Introduction • Not what you know, but who you know • A social network plays a fundamental role as a medium for the spread of information, ideas, and influence • We develop user-centric modeling technology • Dynamically describe and update a PSN • Infer , predict and filter some questrions

4. Overview

5. CommunityNet • Personal Social Network • ERGM (p* model) • Content-Time-Relation Algorithm • Predictive Algorithm

6. CTR Algorithm • Joint probabilistic model • Sources • email content • Sender and receiver information • Time stamps

7. CTR algorithm • Training phase • Input: old information from emails (content, sender, and receiver) • Output: • Steps: • Estimate • Estimate

8. CTR algorithm • Testing phase • Input: new emails with content and time stamps • Output: • Steps • Estimate • Estimate • Update the model by incorporate the new topics

9. Inference, filtering, prediction • Q1: Which is to answer a question of whom we should send the message d to during the time period t? • Q2: If we receive an email, who will be possibly the sender?

10. Predictive algorithm • Use personal social network model • Use LDA combined with PSN model • Use CTR model • Use Adaptive CTR model • Aggregative update : t(0) ~ t(i-1) • Recent data update : t(i-n) ~ t(i-1) • sliding window: choose efficient data

11. Community Analysis • Topic analysis • Topic distribution • Topic trend analysis • Prediction Community patterns • share information int the community

12. Individual Analysis • Role Discovery • Predicting Receivers • Inferring Senders • Adaptive Prediction

13. Role Discovery • Show how people’s roles in an event

14. Predicting Receivers • Infer who will possibly be the receivers by • historic communication records • the content of the email-to-send

15. Inferring Senders • Infer who will possibly be the senders by • Person’s CommunityNet • The email content

16. Adaptive Prediction • Apply adaptive algorihtm to solve the email change problem over time

17. Adaptive Prediction

18. Community Applications • Sensing Informal Networks • Personal Social Network • Personal Topic-Community Network • Personal Social Capital Management-Receiver Recommendation Demo

19. Personal Social Network

20. Personal Social Network

21. Personal Social Network

22. Personal Topic-Community Network

23. Personal Social Capital Management-Receiver Recommendation Demo

24. Personal Social Capital Management-Receiver Recommendation Demo

25. Conclusions • CTR algorithm incorporates contact, content, and time information simultaneously • CommunityNet can model and predict the community behavior as well as personal behavior • Multi-modality algorithm performs better than both the social network-based and content-based predictions