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

Outline Introduction CommuntiyNet Community Analysis Individual Analysis CommunityNet Applications Conclusions

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

Overview

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

CTR Algorithm Joint probabilistic model Sources content Sender and receiver information Time stamps

CTR algorithm Training phase Input: old information from s (content, sender, and receiver) Output: Steps: Estimate Estimate

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

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 , who will be possibly the sender?

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

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

Individual Analysis Role Discovery Predicting Receivers Inferring Senders Adaptive Prediction

Role Discovery Show how people’s roles in an event

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

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

Adaptive Prediction Apply adaptive algorihtm to solve the change problem over time

Adaptive Prediction

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

Personal Social Network

Personal Topic-Community Network

Personal Social Capital Management- Receiver Recommendation Demo

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