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We examined the relationship between political affiliation, perceptual (percentage, slope) estimates, and subjective judgements of disease prevalence and mortality across three chart types. An online survey (N = 787) exposed separate groups of participants to charts displaying (a) COVID-19 data or (b) COVID-19 data labeled ‘Influenza (Flu)’. Block 1 examined responses to cross-sectional mortality data (bar graphs, treemaps); results revealed that perceptual estimates comparing mortality in two countries were similar across political affiliations and chart types (all ps > .05), while subjective judgements revealed a disease x political party interaction ( p < .05). Although Democrats and Republicans provided similar proportion estimates, Democrats interpreted mortality to be higher than Republicans; Democrats also interpreted mortality to be higher for COVID-19 than Influenza. Block 2 examined responses to time series (line graphs); Democrats and Republicans estimated greater slopes for COVID-19 trend lines than Influenza lines ( p < .001); subjective judgements revealed a disease x political party interaction ( p < .05). Democrats and Republicans indicated similar subjective rates of change for COVID-19 trends, and Democrats indicated lower subjective rates of change for Influenza than in any other condition. Thus, while Democrats and Republicans saw the graphs similarly in terms of percentages and line slopes, their subjective interpretations diverged. While we may see graphs of infectious disease data similarly from a purely mathematical or geometric perspective, our political affiliations may moderate how we subjectively interpret the data.

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Designers' use of deceptive and manipulative design practices have become increasingly ubiquitous, impacting users' ability to make choices that respect their agency and autonomy. These practices have been popularly defined through the term "dark patterns" which has gained attention from designers, privacy scholars, and more recently, even legal scholars and regulators. The increased interest in the term and underpinnings of dark patterns across a range of sociotechnical practitioners intrigued us to study the evolution of the concept, to potentially speculate the future trajectory of conversations around dark patterns. In this paper, we examine the history and evolution of the Twitter discourse through #darkpatterns from its inception in June 2010 until April 2021, using a combination of quantitative and qualitative methods to describe how this discourse has changed over time. We frame the evolution of this discourse as an emergent transdisciplinary conversation that connects multiple disciplinary perspectives through the shared concept of dark patterns, whereby these participants engage in a conversation marked by socio-technical angst in order to identify and fight back against deceptive design practices. We discuss the potential future trajectories of this discourse and opportunities for further scholarship at the intersection of design, policy, and activism.CCS Concepts: • Human-centered computing → Social network analysis; HCI design and evaluation methods; Collaborative and social computing.

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