Alexandria Digital Research Library

Information Propagation on Social Networks

Author:
Busch, Michael J.
Degree Grantor:
University of California, Santa Barbara. Mechanical Engineering
Degree Supervisor:
Jeff Moehlis
Place of Publication:
[Santa Barbara, Calif.]
Publisher:
University of California, Santa Barbara
Creation Date:
2014
Issued Date:
2014
Topics:
Statistics, Engineering, Mechanical, Web Studies, and Applied Mathematics
Keywords:
Classification
Mean-field Model
Epidemiology
Social Network
Information Propagation
Kalman Filter
Genres:
Dissertations, Academic and Online resources
Dissertation:
Ph.D.--University of California, Santa Barbara, 2014
Description:

Many models of disease and rumor spreading phenomena average the behavior of individuals in a population in order to obtain a coarse description of expected system behavior. For these types of models, we determine how close the coarse population-level approximation is to its corresponding agent-based system and discuss the accuracy of the population-level approximation. We apply these theoretical results to real social network data to see how well they describe the contagious nature of social phenomena. Specifically, we consider hashtag adoption data collected from the Twitter social network. To assimilate the Twitter data to a simple contagion model, we developed and implemented statistical learning methods to construct an adaptive state estimator for systems described by nonlinear stochastic differential equations.

We found that the static network structure alone is not sufficient for explaining hashtag adoption among users in the Twitter social network, and our result suggest that a user-centric model would be more appropriate for this task. We propose a model for individual social media users, termed a genotype, which is a per-topic summary of a user's interest, activity and susceptibility to adopt new information. We show that the genotype framework is capable of accurately quantifying the adoption behavior of individual users with respect to hashtag topics.

Physical Description:
1 online resource (159 pages)
Format:
Text
Collection(s):
UCSB electronic theses and dissertations
ARK:
ark:/48907/f3p55kn5
ISBN:
9781321567519
Catalog System Number:
990045118030203776
Rights:
Inc.icon only.dark In Copyright
Copyright Holder:
Michael Busch
File Description
Access: Public access
Busch_ucsb_0035D_12415.pdf pdf (Portable Document Format)