The Backstory to "Swaying the Public": A Design Chronicle of Election Forecast Visualizations.

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.

Swaying the Public? Impacts of Election Forecast Visualizations on Emotion, Trust, and Intention in the 2022 U.S. Midterms.

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.

Social Media Sentiment about COVID-19 Vaccination Predicts Vaccine Acceptance among Peruvian Social Media Users the Next Day.

Drawing upon theories of risk and decision making, we present a theoretical framework for how the emotional attributes of social media content influence risk behaviors. We apply our framework to understanding how COVID-19 vaccination Twitter posts influence acceptance of the vaccine in Peru, the country with the highest relative number of COVID-19 excess deaths. By employing computational methods, topic modeling, and vector autoregressive time series analysis, we show that the prominence of expressed emotions about COVID-19 vaccination in social media content is associated with the daily percentage of Peruvian social media survey respondents who are vaccine-accepting over 231 days. Our findings show that net (positive) sentiment and trust emotions expressed in tweets about COVID-19 are positively associated with vaccine acceptance among survey respondents one day after the post occurs. This study demonstrates that the emotional attributes of social media content, besides veracity or informational attributes, may influence vaccine acceptance for better or worse based on its valence.

Sentinel node approach to monitoring online COVID-19 misinformation.

Understanding how different online communities engage with COVID-19 misinformation is critical for public health response. For example, misinformation confined to a small, isolated community of users poses a different public health risk than misinformation being consumed by a large population spanning many diverse communities. Here we take a longitudinal approach that leverages tools from network science to study COVID-19 misinformation on Twitter. Our approach provides a means to examine the breadth of misinformation engagement using modest data needs and computational resources. We identify a subset of accounts from different Twitter communities discussing COVID-19, and follow these 'sentinel nodes' longitudinally from July 2020 to January 2021. We characterize sentinel nodes in terms of a linked domain preference score, and use a standardized similarity score to examine alignment of tweets within and between communities. We find that media preference is strongly correlated with the amount of misinformation propagated by sentinel nodes. Engagement with sensationalist misinformation topics is largely confined to a cluster of sentinel nodes that includes influential conspiracy theorist accounts. By contrast, misinformation relating to COVID-19 severity generated widespread engagement across multiple communities. Our findings indicate that misinformation downplaying COVID-19 severity is of particular concern for public health response. We conclude that the sentinel node approach can be an effective way to assess breadth and depth of online misinformation penetration.

Changes in COVID-19 Vaccine Hesitancy Among Black and White Individuals in the US.

Importance: COVID-19 has disproportionately affected Black individuals in the US; however, vaccination rates among Black individuals trail those among other racial groups. This disparity is often attributed to a high level of vaccine hesitancy among Black individuals, but few studies have examined changes in vaccine hesitancy over time. Objectives: To compare changes in vaccine hesitancy between Black and White individuals in the US and to examine mechanisms that might help explain the observed differences. Design, Setting, and Participants: This survey study used 7 waves of data collected using a panel design. A total of 1200 English-speaking adults in the US were recruited from a nonprobability online panel to construct a census-matched sample. Participants were contacted monthly between December 9, 2020, and June 16, 2021. Main Outcomes and Measures: The main outcome of interest was self-reported vaccination intention, measured on a 6-point scale (where 1 indicates extremely unlikely and 6 indicates extremely likely). Beliefs about the safety, effectiveness, and necessity of COVID-19 vaccines were measured on a 5-point Likert scale, with higher scores denoting greater agreement. Results: The baseline data included 1200 participants (693 women [52.0%; weighted]; 921 White individuals [64.0%; weighted], 107 Black individuals [12.2%; weighted]; weighted mean [SD] age, 49.5 [17.6] years). The survey participation rate was 57.0% (1264 of 2218). Black and White individuals had comparable vaccination intentions in December 2020, but Black individuals experienced larger increases in vaccination intention than White individuals relative to baseline in March 2021 (b = 0.666; P < .001), April 2021 (b = 0.890; P < .001), May 2021 (b = 0.695; P < .001), and June 2021 (b = 0.709; P < .001). The belief that the vaccines are necessary for protection also increased more among Black than White individuals in March 2021 (b = 0.221; P = .01) and April 2021 (b = 0.187; P = .04). Beliefs that the vaccines are safe and effective (b = 0.125; P < .001) and necessary (b = 0.405; P < .001) were positively associated with vaccination intention. There was no evidence that these associations varied by race. Conclusions and Relevance: This survey study suggests that the intention of Black individuals to be vaccinated was initially comparable to that of White individuals but increased more rapidly. There is some evidence that this increase is associated with changes in beliefs about the vaccine. Vaccination rates continue to be lower among Black individuals than White individuals, but these results suggest that this might be less likely the result of vaccine hesitancy than other factors.

Climate change, cultural cognition, and media effects: Worldviews drive news selectivity, biased processing, and polarized attitudes.

According to cultural cognition theory, individuals hold opinions about politically contested issues like climate change that are consistent with their "cultural way of life," conforming their opinions to how they think society should be organized and to what they perceive are the attitudes of their cultural peers. Yet despite dozens of cultural cognition studies, none have directly examined the role of the news media in facilitating these differential interpretations. To address this gap, drawing on a national survey of US adults administered in 2015, we statistically modeled the cultural cognition process in relation to news choices and media effects on public attitudes about climate change. Individuals possessing strongly held cultural worldviews, our findings show, not only choose news outlets where they expect to find culturally congruent arguments about climate change, but they also selectively process the arguments they encounter. Overall, our study demonstrates the substantial role that cultural cognition in combination with news media choices play in contributing to opinion polarization on climate change and other politicized science topics.

Ignorance or bias? Evaluating the ideological and informational drivers of communication gaps about climate change.

Does the relationship between media use and learning about climate change depend more on audiences' scientific literacy on their ideological biases? To answer this question, we evaluate both the knowledge gap and belief gap hypotheses as they relate to climate change. Results indicate belief gaps for news and entertainment content and a knowledge gap for edutainment content. Climate change knowledge among conservatives decreased with greater attention to political news, but increased with greater attention to science news. TV entertainment was associated with a significant decrease in knowledge about climate change among liberals to similar levels as conservatives. Edutainment was associated with a widening gap in knowledge based on respondents' scientific literacy. Implications for informal learning about controversial science through the media are discussed.