People with depression often suffer from severe social seclusion, and the lack of an agreed upon etiology for depression makes it difficult to satisfactorily narrate and "ritually control" it. Focusing on blogs by women with major depression, I delineate the ways in which bloggers publicly express and collaboratively reconstruct their depression narratives. Specifically, using thematic analysis, I argue that depression blogs uniquely bridge between the seclusion that characterizes depression and the exposure offered in blogs, and thus offer people a rare opportunity to publicly share very intimate depression narratives, form communal bonds with their readers, and collaboratively revise their narratives. Depression blogs are also shown to function as "narrative sandboxes"-protected spaces in which bloggers can temporarily and experimentally add or remove different sections from their illness narratives, assess the compatibility of different cultural frameworks, and interchangeably use various metaphors, in an attempt to satisfactorily explain depression.
This article explores the socio-algorithmic construction of identity categories based on an ethnographic study of the Israeli data analytics industry. While algorithmic categorization has been described as a post-textual phenomenon that leaves language, social theory, and social expertise behind, this article focuses on the return of the social—the process through which the symbolic means resurface to turn algorithmically produced clusters into identity categories. I show that such categories stem not only from algorithms’ structure or their data, but from the social contexts from which they arise, and from the values assigned to them by various individuals. I accordingly argue that algorithmic identities stem from epistemic amalgams—complex blends of algorithmic outputs and human expertise, messy data flows, and diverse inter-personal factors. Finally, I show that this process of amalgamation arbitrarily conjoins quantitative clusters with qualitative labels, and I discuss the implausibility of seeing named algorithmic categories as explainable.
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