Systemic view of human-data interaction: analyzing a COVID-19 data visualization platform

Abstract

Human-Data Interaction (HDI) is a growing field concerned by placing humans at the center of data flows and providing mechanisms for people to interact explicitly with systems and data. Understanding HDI from a sociotechnical perspective, we argue that technical and human issues must be approached in an interconnected way throughout a data lifecycle. In this paper, grounding our discussions in a conceptual artifact named Extended Semiotic Framework, we discuss how different interested parties, different levels of signs, and different stages in data lifecycle can affect HDI. We apply this artifact into a local COVID-19 information website and use its results to inform the website redesign. Our discussion and results show the Extended Semiotic Framework as capable to promote a systemic view considering both human and technical issues, as well as to identify problems and challenges at different data stages.