Studying Social Network Sites via computational methods

Social Network Sites (SNSs) are fundamentally
changing the way humans connect, communicate, and relate to
one another and have attracted a considerable amount of
research attention. In general, two distinct research approaches
have been followed in the pursuit of results in this research area.
Firstly, established traditional social science methods, such as
surveys and interviews, have been extensively used for inquiry based research on SNSs. More recently, however, the advent of
Application Programming Interfaces (APIs) has enabled data-centric approaches that have culminated in theory-free “big
data” studies. Both of these approaches have advantages,
disadvantages and limitations that need to be considered in SNS
studies. This PhD work proposes that a suitable combination of
these two approaches can help understand the limitations and
address the shortcomings that researchers are faced with when
following each approach separately. In order to illustrate the
practicability and value of this combination of approaches, I
propose to employ usage and network data collected via an API
to complement survey metrics in two SNS studies. The first study
examines the motivations for using Facebook and Twitter, in
order to enhance our understanding of how and why people use
these services, while the second study focuses on aspects of the
interpersonal relationships on Facebook, such as tie strength,
trust and information disclosure.