For researchers who are unfamiliar with “digital social science” and “digital methods”, it may seem like an entirely new—and intimidating—realm. It’s true that digital environments offer novel types of data, and sometimes at quite a different scale. However, the basic tenets of sound research practices remain the same in digital spaces as they do in non-digital spaces. As ever, the chosen method needs to match the research question; taking access, context, and collection sources into account is crucial; understanding what your collected data actually represents is foundational; and designing (and executing) your project with a nuanced and comprehensive ethical approach is obligatory.
In many ways, there is a lot of overlap between “digital” methods and more traditional methods. For example, online interviews, digital ethnographies, and internet-based surveys rely on many of the same methodological practices and concepts as their analog counterparts. There are also a range of newer methods that allow for the exploration of digital formats and the novel forms of data that those formats generate from apps to websites to operating systems. What is important to remember is that doing digital methods does not require fully retraining yourself in an entirely new discipline and set of methodologies: if you are a qualitative researcher by training, you do not need to (and likely should not!) immediately become an expert in social network analysis and data visualization, for example. In many ways, becoming a digital researcher means applying your already-developed research skills to digital contexts.
If your project is suddenly shifting to a digital-only context, your research question and ethical concerns very well may have to change; however, it is important to remember that “online” and “offline” has always been, and remains, a false dichotomy. People live their lives across many contexts, both digital and analog, and these different forms and contexts of interaction have varied affordances that create multiple types of data that can be triangulated to offer a fuller—if necessarily partial—picture.