Communication research has recently had an influx of groundbreaking findings based on big data. Examples include not only analyses of Twitter, Wikipedia, and Facebook, but also of search engine and smartphone uses. These can be put together under the label “digital media.” This article reviews some of the main findings of this research, emphasizing how big data findings contribute to existing theories and findings in communication research, which have so far been lacking. To do this, an analytical framework will be developed concerning the sources of digital data and how they relate to the pertinent media. This framework shows how data sources support making statements about the relation between digital media and social change. It is also possible to distinguish between a number of subfields that big data studies contribute to, including political communication, social network analysis, and mobile communication.
One of the major challenges is that most of this research does not fall into the two main traditions in the study of communication, mass and interpersonal communication. This is readily apparent for media like Twitter and Facebook, where messages are often distributed in groups rather than broadcast or shared between only two people. This challenge also applies, for example, to the use of search engines, where the technology can tailor results to particular users or groups (this has been labeled the “filter bubble” effect). The framework is used to locate and integrate big data findings in the landscape of communication research, and thus to provide a guide to this emerging area.