A year ago, we submitted an IEEE VIS paper entitled "Swaying the Public? Impacts of Election Forecast Visualizations on Emotion, Trust, and Intention in the 2022 U.S. Midterms" [50], which was later bestowed with the honor of a best paper award. Yet, studying such a complex phenomenon required us to explore many more design paths than we could count, and certainly more than we could document in a single paper. This paper, then, is the unwritten prequel-the backstory. It chronicles our journey from a simple idea-to study visualizations for election forecasts-through obstacles such as developing meaningfully different, easy-to-understand forecast visualizations, crafting professional-looking forecasts, and grappling with how to study perceptions of the forecasts before, during, and after the 2022 U.S. midterm elections. This journey yielded a rich set of original knowledge. We formalized a design space for two-party election forecasts, navigating through dimensions like data transformations, visual channels, and types of animated narratives. Through qualitative evaluation of ten representative prototypes with 13 participants, we then identified six core insights into the interpretation of uncertainty visualizations in a U.S. election context. These insights informed our revisions to remove ambiguity in our visual encodings and to prepare a professional-looking forecasting website. As part of this story, we also distilled challenges faced and design lessons learned to inform both designers and practitioners. Ultimately, we hope our methodical approach could inspire others in the community to tackle the hard problems inherent to designing and evaluating visualizations for the general public.

We conducted a longitudinal study during the 2022 U.S. midterm elections, investigating the real-world impacts of uncertainty visualizations. Using our forecast model of the governor elections in 33 states, we created a website and deployed four uncertainty visualizations for the election forecasts: single quantile dotplot (1-Dotplot), dual quantile dotplots (2-Dotplot), dual histogram intervals (2-Interval), and Plinko quantile dotplot (Plinko), an animated design with a physical and probabilistic analogy. Our online experiment ran from Oct. 18, 2022, to Nov. 23, 2022, involving 1,327 participants from 15 states. We use Bayesian multilevel modeling and post-stratification to produce demographically-representative estimates of people's emotions, trust in forecasts, and political participation intention. We find that election forecast visualizations can heighten emotions, increase trust, and slightly affect people's intentions to participate in elections. 2-Interval shows the strongest effects across all measures; 1-Dotplot increases trust the most after elections. Both visualizations create emotional and trust gaps between different partisan identities, especially when a Republican candidate is predicted to win. Our qualitative analysis uncovers the complex political and social contexts of election forecast visualizations, showcasing that visualizations may provoke polarization. This intriguing interplay between visualization types, partisanship, and trust exemplifies the fundamental challenge of disentangling visualization from its context, underscoring a need for deeper investigation into the real-world impacts of visualizations. Our preprint and supplements are available at https://doi.org/osf.io/ajq8f.