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How can social scientists work together with research participants to create new knowledge on issues of public concern? Our experiences studying Instagram with young adults showed us that collaborative theorizing is not only possible, but also that it can teach everyone involved valuable lessons.<\/p>\n
At the outset of our research project<\/a> \u201cComing of Age on Instagram,\u201d we noted a paradox concerning public knowledge about social media. On the one hand, users acquire expertise about platforms as they use them on a daily basis and figure out how to do so strategically. On the other hand, public debates about social media are often encased in anxious narratives of harm and risk that blot out such distributed expertise. We wanted to see what we could learn about social media by talking to<\/em> users rather than merely talking about<\/em> them. That sounds straightforward enough, but it turned out that, as social scientists, we face challenges in working collaboratively.<\/p>\n That is because social scientists have long worked on the assumption that they have a better grasp on why people do things than the people doing those things. This goes back to the founding figures of anthropology and sociology. \u00c9mile Durkheim<\/a> thought the point of social scientific inquiry was to uncover the \u201csocial facts\u201d that were operative behind the phenomena we study, independent from anyone\u2019s individual consciousness. Talking to the people involved in making those social phenomena happen is a distraction at best, perhaps even counterproductive. Those with a more interpretive bent, such as Max Weber<\/a>, thought that understanding the meanings that actors themselves attach to what they do is important. However, the actual practice of interpretive social science often still enthrones the researcher as the one discerning the meanings guiding social action.<\/p>\n For many years, Grounded Theory (GT) has been the most popular approach to interpretive analysis across several social sciences, including anthropology and sociology. GT was first proposed by Barney Glaser and Anselm Strauss, and was later (after the two had a falling out) elaborated by other scholars such as Kathy Charmaz. GT is an iterative approach to building concepts and constructing theory from qualitative data. It usually entails several \u201cpasses\u201d of coding (labeling) qualitative data, such as interview transcripts and fieldnotes, with the aim of identifying recurring themes that \u201cemerge\u201d from the material. After each pass, the researcher may opt to return to their research site to gather additional material on previously underexplored themes until they find they have reached a saturation point. At later stages, the researcher usually aggregates the codes assigned in earlier stages, first into \u201caxial codes,\u201d and later into concepts. Because the concepts are closely tied to themes identified in the qualitative material in an inductive manner, they are \u201cgrounded\u201d in the data rather than, for instance, the researcher\u2019s preconceptions or theoretical hobby horses. Even though codebooks, measures of intercoder reliability, and (more recently) computational techniques<\/a> for pattern recognition allow the GT process to be a team effort, it generally unfolds without research participants present. Thus, despite recognizing the need for the creation of knowledge about the social world to be grounded in first-hand observations, the separation between data collection and data analysis upholds the epistemically privileged status of the researcher.<\/p>\n GT is undisputedly the mainstream in qualitative data analysis \u2013 The Invention of Grounded Theory<\/em> by Glaser and Strauss has been cited more often than volume 1 of Marx\u2019s Capital<\/em> \u2013 but it is not the only game in town. Research traditions like Kurt Lewin\u2019s action research, marxian workers\u2019 inquiry, or Freirian and feminist participatory methods seek to break down the division between researchers and researched. The more recent trend of citizen science usually involves nonacademic communities in data collection in their own environments, while data analysis and theorizing often remain the tasks of academic experts.<\/p>\n In our project, we wanted to advance collaborative theorizing<\/em>. One way we did so was through a session at the Digital Methods Initiative\u2019s 2024 Winter School. The DMI at the University of Amsterdam has a history<\/a> of almost two decades of \u201crepurposing the web for social and cultural research\u201d. Summer and Winter Schools consist of week-long \u201csprints\u201d during which groups of participants complete research projects centered on what Richard Rogers calls<\/a> \u201cnatively digital\u201d datasets.<\/p>\n Our session involved both pre-existing qualitative data (transcripts of in-depth interviews conducted in 2022) and autoethnographic data provided by participants in our session. For the latter, participants were asked to send DMs (direct messages) to themselves twice a day over the course of one week before the start of the Winter School to record how they used Instagram to document, recall or narrate parts of their lives. Similarly to diary studies<\/a> in communication research or \u201cexperience samples<\/a>\u201d used in the psychology of everyday life, these DMs provided granular, first-person insight into an underexplored aspect of how people use Instagram: not just as a means of presenting themselves to others, but also as a way of assembling their biographies. These messages became the basis for our collaborative autoethnography.<\/p>\n On the first day of the Winter School, after introducing our group topic, the twelve participants interviewed each other about their diaries. In this manner they gained insights into similarities and differences between their individual experiences. Because at times participants recorded quite intimate details from their lives, analyzing them in a collaborative context was ethically challenging<\/a>. We addressed this challenge with the group of participants, and actively worked on creating a safe and open research space. We shared regular check-in moments to discuss how it felt to share one\u2019s stories with the group.<\/p>\n Equipped with these initial insights, they spent the afternoon studying extant literature on memory, biography, and life writing in search of concepts that resonated with their incipient ideas. This was followed by a group discussion and an initial mind map to develop a shared analytic vocabulary. We repeated this kind of joint discussion throughout the sprint, gradually sharpening our concepts through constant comparison. The following day, participants read transcripts of in-depth interviews with Instagram users of a similar background (international students pursuing master\u2019s degrees). This provided further opportunity to reflect on their own experiences and put them in a wider context. They were instructed to write a vignette based on the interview transcript that conveyed the interviewee\u2019s experiences with Instagram as a memory device. Through this exercise, we discovered new patterns that further enriched our analysis.<\/p>\n As we were preparing to share the results of our sprint with other Winter School participants in the form of a poster, we struggled to condense our ideas into concepts. As facilitators of the process, our main challenge was to give up control of the process and allow our participants to decide on a way to organize our ideas. The GT process, as described above, usually unfolds over a longer duration, and our approximation of this analytic process in a condensed time frame at times felt chaotic and unstructured. Our participants, however, reported something very different. Overwhelmingly, they felt that the process led them toward a \u201clogical conclusion\u201d as their ideas gradually took shape through an abductive process<\/a>.<\/p>\nWho knows<\/h3>\n
Theorizing together<\/h3>\n
Collaborative methods<\/h3>\n
Theoretical and pedagogical lessons<\/h3>\n