Social media data have substantially enlarged the potential pools of evidence in the study of variation and change in English. They offer access to language use of large numbers of informants, but the downside is that they contain inadequate social background information. This seriously restricts the theoretical insight, limiting investigations to the study of actuation of change and leaving out those that focus on diffusion. It is widely held that while actuation is largely functional, diffusion requires information on the broader social structures within which speakers belong. We present an algorithmic method for adding social information to Twitter. Our method builds on interaction parameters (size and structure of networks, similarity, and communication frequency). Using such participant-centred information as a proxy for social information increases the empirical validity substantially. In addition, the article presents a case study of how networks of varying strength condition ongoing change. The data are drawn from five metropolitan centres in the UK and from English as a lingua franca setting in the Nordic region. The results suggest that network size plays an important, yet understudied, role in the social network theory in sociolinguistics.