High Performance Computational Social Science Modeling of Networked Populations
Abstract
Dynamics of social processes in populations, such
as the spread of emotions, influence, opinions, and
mass movements (often referred to
individually and collectively as contagions),
are increasingly studied because of their economic,
social, and political impacts. Moreover, multiple
contagions may interact and hence studying their
simultaneous evolution is important. Within the
context of social media, large datasets involving
many tens of millions of people are leading to new
insights into human behavior, and these datasets
continue to grow in size. Through social media,
contagions can readily cross national boundaries, as evidenced
by the 2011 Arab Spring. These and other observations
guide our work.
Our goal is to study contagion processes at scale
with an approach that permits intricate descriptions of
interactions among members of a population.
Our contributions are a modeling environment
to perform these computations and a set of approaches
to predict contagion spread size and to
block the spread of contagions. Since we represent
populations as networks, we also provide
insights into network structure effects, and
present and analyze a new model of contagion dynamics that
represents a person\'s behavior in repeatedly joining and
withdrawing from collective action. We study variants of
problems for different classes of social contagions, including those
known as simple and complex contagions.
as the spread of emotions, influence, opinions, and
mass movements (often referred to
individually and collectively as contagions),
are increasingly studied because of their economic,
social, and political impacts. Moreover, multiple
contagions may interact and hence studying their
simultaneous evolution is important. Within the
context of social media, large datasets involving
many tens of millions of people are leading to new
insights into human behavior, and these datasets
continue to grow in size. Through social media,
contagions can readily cross national boundaries, as evidenced
by the 2011 Arab Spring. These and other observations
guide our work.
Our goal is to study contagion processes at scale
with an approach that permits intricate descriptions of
interactions among members of a population.
Our contributions are a modeling environment
to perform these computations and a set of approaches
to predict contagion spread size and to
block the spread of contagions. Since we represent
populations as networks, we also provide
insights into network structure effects, and
present and analyze a new model of contagion dynamics that
represents a person\'s behavior in repeatedly joining and
withdrawing from collective action. We study variants of
problems for different classes of social contagions, including those
known as simple and complex contagions.
Collections
- Doctoral Dissertations [12719]