Understanding (Offline/Online) Society: Linking survey and digital trace data

Background

Understanding online behaviours, attitudes and identities is a key challenge for social science in the 21st century. At the same time, the opportunities provided by digital trace data (DTD) are substantial as researchers can access huge quantities of precise observational data often relatively quickly, easily and cheaply. However, that these data are not designed for social researchers creates challenges; researchers have a limited understanding of who (or what in the case of 'bots') is included in the data and the biases it may therefore have, or control over what information is collected to make sure it answers their research questions. 

Aims

This project explores the feasibility of, and opportunities afforded by, linking together survey and digital trace data. By doing so, survey data can benefit from additional data covering areas not included in the original questionnaire and outside a single point in time. At the same time, DTD can benefit from the structure and direction of survey data - a sample frame and purposeful questions to assist with analysis. 

The project has looked specifically at linking X (formerly Twitter) and survey data. As a platform that is relatively popular in the UK, X/Twitter holds information that could easily be applied in a social research context, is relatively easy to identify an individual on, and (until recently) was relatively open. However, we have recently extended this work to look at LinkedIn and believe the findings can be applied to digital trace data more broadly.