The coronavirus pandemic has made ethnographic fieldwork, as traditionally conceived in anthropology, temporarily impossible to conduct. Facing long-term limitations to mobility and physical contact, which will challenge our research practices for the foreseeable future, social anthropology has to adjust to these new circumstances. This article discusses and reflects on what digital ethnography can off er to researchers across the world, providing critical insight into the method and offering advice to beginners in the field. Last, but not least, the article introduces the phrase ‘anthropology from home’ to talk about research in the pandemic times – that is, geographically restricted but digitally enabled.

The use of data and algorithms in the social sciences allows for exciting progress, but also poses epistemological challenges. Operations that appear innocent and purely technical may profoundly influence final results. Researchers working with data can make their process less arbitrary and more accountable by making theoretically grounded methodological choices. We apply this approach to the problem of simplifying networks representing ethnographic corpora, in the interest of visual interpretation. Network nodes represent ethnographic codes, and their edges the co-occurrence of codes in a corpus. We introduce and discuss four techniques to simplify such networks and facilitate visual analysis. We show how the mathematical characteristics of each one are aligned with an identifiable approach in sociology or anthropology: structuralism and post-structuralism; identifying the central concepts in a discourse; and discovering hegemonic and counter-hegemonic clusters of meaning. We then provide an example of how the four techniques complement each other in ethnographic analysis.

The use of data and algorithms in the social sciences allows for exciting progress, but also poses epistemological challenges. Operations that appear innocent and purely technical may profoundly influence final results. Researchers working with data can make their process less arbitrary and more accountable by making theoretically grounded methodological choices. We apply this approach to the problem of reducing networks representing ethnographic corpora. Their nodes represent ethnographic codes, and their edges the co-occurrence of codes in a corpus. We introduce and discuss four techniques to reduce such networks and facilitate visual analysis. We show how the mathematical characteristics of each one are aligned with a specific approach in sociology or anthropology: structuralism and post-structuralism; identifying the central concepts in a discourse; and discovering hegemonic and counter-hegemonic clusters of meaning.