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.