Beyond the Coded Gaze

Abstract

In CSCW and HCI, work examining expression of mental health and illness on social media frequently aims to classify content, quantify visual trends, and predict user states. This approach to analysis is a form of the coded gaze, a type of algorithmic 'way of seeing' coined with respect to artificial intelligence techniques. The coded gaze classifies content through researcher- and machine-labeled categories, relying on a series of theoretical assumptions that influence how values pertaining to mental health and illness become inscribed in data. In this paper, we build upon this research to support alternative methods of data interpretation. We join manual collection of Instagram posts with semi-structured interviews and digital ethnography over six months to understand how Instagram users express their experiences with mental health and illness. We argue that individuals negotiate claims to mental health and illness through visibility and signaling, the boundaries between mental health and illness are porous and blurred, and reposting and remix are a form of participation. We discuss practical and ethical implications for studying the expression of mental health and illness online.

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