Patterns of Surprise and Ambivalence: Studying Social Media Visuality by Way of Aggregated Autoethnography

Abstract

Visuality is central in social media experiences, but complex to research. In this paper, we introduce aggregated autoethnography for nuanced analysis of socially mediated visual practices. The approach starts from guided autoethnographies which help to empower participants to explore their own experiences and build thick descriptions, and moves through multiple levels of aggregation, integration and synthesis (from individual autoethnographies to national datasets of coded snippets, to datasets specific to arguments emerging out of multinational patterns). The aggregated autoethnography approach makes unexpected topics accessible; offers dynamic, rather than static insight; makes visible that which is routine and tacit, as well as that which is experienced as ambivalent. Further, aggregation allows synthesis of multiple perspectives, revealing patterns across contexts that are otherwise difficult to detect. The approach detailed here is used to move back and forth between the singular pieces of visual content and the flows they are part of; to remain loyal to the situational perspective that the visual communication becomes meaningful in; to capture relevant artifacts as well as people's practices; and to be mindful of the affective, embodied and material aspects of ways of seeing with social media.