Abstract Details
Activity Number:
|
336
|
Type:
|
Topic Contributed
|
Date/Time:
|
Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Section on Statistics in Marketing
|
Abstract - #308085 |
Title:
|
Modeling Endogeneity in the Formation of Trust Relationships Online
|
Author(s):
|
William Rand*+ and Hossam Sharara and Lise Getoor
|
Companies:
|
Center for Compexity in Business and Google and University of Maryland
|
Keywords:
|
trust ;
viral marketing ;
agent-based modeling ;
social networks ;
mechanism design ;
diffusion
|
Abstract:
|
Viral marketing uses the existing social network between customers to spread information about products and encourage product adoption. Existing viral marketing models focus on the dynamics of the diffusion process, however they typically: (a) only consider a single product campaign and (b) fail to model the evolution of the social network, as the trust between individuals changes over time, during the course of multiple campaigns. In this work, we propose an adaptive viral marketing model which captures: (1) multiple different product campaigns, (2) the diversity in customer preferences among different product categories, and (3) changing confidence in peers' recommendations over time. By applying our model to a real-world network extracted from the Digg social news website, we provide insights into the effects of network dynamics on the different products' adoption. Our experiments show that our proposed model outperforms earlier non-adaptive diffusion models in predicting future product adoptions. We also show how this model can be used to explore new viral marketing strategies that are more successful than classic strategies which ignore the dynamic nature of social networks.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.