Quantifying Visualization Vibes: Measuring Socio-Indexicality at Scale

TL;DR

This paper develops an analytic framework to measure how visualizations evoke social inferences about their origin, revealing that such perceptions are widespread, influence trust, and are affected by design features, with implications for public data communication.

Contribution

It introduces a novel framework for analyzing social provenance in visualizations and provides empirical evidence on how social inferences are formed and influenced by design.

Findings

Social inferences about visualizations are widespread and not limited to specific groups.

Design features influence perceptions of social provenance.

Social inferences can affect trust in visualizations.

Abstract

What impressions might readers form with visualizations that go beyond the data they encode? In this paper, we build on recent work that demonstrates the socio-indexical function of visualization, showing that visualizations communicate more than the data they explicitly encode. Bridging this with prior work examining public discourse about visualizations, we contribute an analytic framework for describing inferences about an artifact's social provenance. Via a series of attribution-elicitation surveys, we offer descriptive evidence that these social inferences: (1) can be studied asynchronously, (2) are not unique to a particular sociocultural group or a function of limited data literacy, and (3) may influence assessments of trust. Further, we demonstrate (4) how design features act in concert with the topic and underlying messages of an artifact's data to give rise to such 'beyond-data' readings. We conclude by discussing the design and research implications of inferences about social provenance, and why we believe broadening the scope of research on human factors in visualization to include sociocultural phenomena can yield actionable design recommendations to address urgent challenges in public data communication.