Type
Text
Type
Dissertation
Advisor
Gao, Jie | Skiena, Steven | van de Rijt, Arnout | Liu, Juan.
Date
2015-08-01
Keywords
Computer science
Department
Department of Computer Science.
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/77305
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
Abstract
As the complexity of online activities increases, social network structures have come to play an increasingly important role in the experience and effectiveness of an individual's online life. These structures exhibit a high degree of dynamism. Also, online platforms have provided us with unprecedented opportunities to study behavior and dynamics of these network structures. Understanding whether/why/when a person will behave in a certain manner can be important in a number of social domains. In our work, we model these network dynamics and design accurate prediction algorithms for these behavioral models. The main challenges that we address in this dissertation are 1) the ability to predict imminent departure events and the probable adverse impact of these events, 2) understanding the processes that drive group growth and stability, and 3) implications of social influence on opinion and relationship formation. Our work offers interesting insights on factors that cause and affect these network events. The methods proposed in our study use a diverse set of features that help us in building richer predictive models that result in more accurate predictions. Another aspect of our research deals with the innovative use of spectral graph theory concepts in unifying activity information of people across different social platforms. We also contribute in the development of a large-scale news and blog analysis engine that provides ready access to a wealth of interesting statistics on millions of people, places, and things across a number of interesting web corpora. The work we present has a wide range of applications: helping spot malicious behavior, forecasting group stability, predicting churn, recommending better content, deanonymizing network identities, and detecting trends in news data. Our techniques have been evaluated and validated on several large-scale, real-world datasets that span different domains. | 177 pages
Recommended Citation
Patil, Akshay, "Analyzing Dynamics in Online Social Networks" (2015). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 3126.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/3126