The era of 'big data' studies and computational social science has recently given rise to a number of realignments within and beyond the social sciences, where otherwise distinct data formats-digital, numerical, ethnographic, visual, etc.-rub off and emerge from one another in new ways. This article chronicles the collaboration between a team of anthropologists and sociologists, who worked together for one week in an experimental attempt to combine 'big' transactional and 'small' ethnographic data formats. Our collaboration is part of a larger cross-disciplinary project carried out at the Danish Technical University (DTU), where high-resolution transactional data from smartphones allows for recordings of social networks amongst a freshman class (N ¼ 800). With a parallel deployment of ethnographic fieldwork among the DTU students, this research setup raises a number of questions concerning how to assemble disparate 'data-worlds' and to what epistemological and political effects? To address these questions, a specific social event-a lively student party-was singled out from the broader DTU dataset. Our experimental collaboration used recordings of Bluetooth signals between students' phones to visualize the ebb and flow of social intensities at the DTU party, juxtaposing these with ethnographic field-notes on shifting party atmospheres. Tracing and reflecting on the process of combining heterogeneous data, the article offers a concrete case of how a 'stitching together' of digital and ethnographic data-worlds might take place.

This article proposes a theory of how interaction in groups influences differential participation in political activism and interrogates this theory through an empirical analysis of online Facebook group interaction. We study the refugee solidarity movement in a mixed methods design employing online ethnography, survey, and “big” social media data. Instead of conceptualizing the group as a social network or social movement organization (SMO), we argue that the group’s culture emerges as patterns of interaction that have implications for what kind of activities in which group members participate. Based on observations from our online ethnography, we suggest that group interaction influences differential individual participation through processes of (1) encoding different habits and (2) attuning the activist to different aspects of situations. We support our theoretical propositions with six statistical tests of the relationship between the group-level variable of contentious group style and the individual-level variable of participation in political protest. The dependent variable, political protest, and a comprehensive set of controls stem from an original survey of the Danish refugee solidarity movement with 2,283 respondents. We link the survey data with “big” social media data used to estimate the focal explanatory variable, contentious group style, generated from content analysis of online interaction in 119 Facebook groups quantified with supervised machine learning. The results show that group style has a consistently positive relationship with the individual’s degree of participation independent of networks, SMO framing, and individual attributes.

Acting in solidarity with deprived others has become a central topic in social movement research. The explanations of solidarity activism or political altruism are few. However, social movement researchers have claimed that solidarity with out‐of‐group others is a by‐product of in‐group interaction. In contrast, we argue that out‐group interaction with the deprived other and the formation of a solidary relationship is central to the ebb and flow of solidarity activism. We investigate the Danish refugee solidarity movement and show that the meeting with the deprived other 1) brings about an interaction order which makes an ethical demand on the activists to care for the other both within the bounds of the situations and in the future; 2) enacts and amplifies activists’ values and beliefs because the deprived other becomes an exemplar of the injustice and the need to help the broader group of people in the same fragile situation. We develop and test this theory drawing on 42 life‐history interviews and a social media dataset containing a panel of 87,455 activists participating in refugee solidarity groups.

The size and variation in both meaning-making and populations that characterize much contemporary text data demand research processes that support both discovery, interpretation and measurement. We assess one dominant strategy within the social sciences that takes a computer-led approach to text analysis. The approach is coined computational grounded theory. This strategy, we argue, relies on a set of unwarranted assumptions, namely, that unsupervised models return natural clusters of meaning, that the researcher can understand text with limited immersion and that indirect validation is sufficient for ensuring unbiased and precise measurement. In response to this criticism, we develop a framework that is computer assisted. We argue that our reformulation of computational grounded theory better aligns with the principles within grounded theory, anthropological theory generation and ethnography.