Twitter in the Analysis of Social Phenomena: An Interpretative Framework

Abstract

AbstractAs the use of online services grows and capabilities in data storage as well as analytics keep on rising, researchers become increasingly interested in what digital trace data might tell them about patterns of human behavior. These data sources potentially hold new information on the mechanisms of human interaction and social phenomena. But before we can unlock the potential of these data, we have to establish how they are connected with social phenomena of interest. While there is an increasing body of research linking offline phenomena to patterns in digital trace data, there is surprisingly little research focusing on the mechanisms leading users to interact with online services. This is a serious deficit in the literature, as digital data traces do not offer us a direct view on social phenomena or events; instead, they offer us a view of reality mediated through the interests and behavior of users moving in the constricted, semi-public communication spaces provided by various digital services. In this chapter, I develop a framework for the use of digital trace data in social science. Key to this framework is a realization that the reflection of reality emerging from digital trace data might be biased through various mediating factors leading users to interact with digital services. This chapter will discuss the nature of digital trace data, various approaches for their use in the analysis of social phenomena, and close by introducing a framework for their application in social science.KeywordsSocial PhenomenonOnline ServicePolitical EventTwitter UserDigital MethodThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.