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.

Vaccine Hesitancy Under the Magnifying Glass: A Systematic Review of the Uses and Misuses of an Increasingly Popular Construct.

Resistance to vaccines has hindered attempts to contain and prevent outbreaks of infectious diseases for centuries. More recently, however, the term "vaccine hesitancy" has been used to describe not necessarily outright resistance but also a delay in acceptance or uncertainty regarding vaccines. Given concerns about hesitancy and its impact on vaccine uptake rates, researchers increasingly shifted the focus from resistance to vaccines toward vaccine hesitancy. Acknowledging the urgency to accurately assess the phenomenon, it is critical to understand the state of the literature, focusing on issues of conceptualization and operationalization. To carry out this systematic review, we collected and analyzed all published empirical articles from 2000 to 2021 that explicitly included quantitative self-report measures of vaccine hesitancy (k = 86). Using a mixed-method approach, the review demonstrates and quantifies crucial inconsistencies in the measurement of the construct, lack of clarity in regard to the determination of who should or should not be defined as hesitant, and overreliance on unrepresentative samples. Crucially, our analysis points to a potential systematic bias toward exaggerating the level of hesitancy in the population. Modeling a vaccine hesitancy co-citation network, the analysis also points to the existence of insular academic silos that make it harder to achieve a unified measurement tool. Theoretical and practical implications for academics, practitioners, and policymakers are discussed.

Vaccine discourse in white nationalist online communication: A mixed-methods computational approach.

INTRODUCTION: Research has indicated a growing resistance to vaccines among U.S. conservatives and Republicans. Following past successes of the far-right in mainstreaming health misinformation, this study tracks almost two decades of vaccine discourse on the extremist, white nationalist (WN) online message-board Stormfront. We examine the argumentative repertoire around vaccines on the forum, and whether it assimilated to or challenged common arguments for and against vaccines, or extended it in ways unique to the racist WN agenda. METHODS: We use a mixed-methods approach, combining unsupervised machine learning of 8892 posts including the term "vaccin*", published on Stormfront between 2001 and 2017. We supplemented the computational analysis with a manual coding of randomly sampled 500 posts, evaluating the prevalence of pro- and anti-vaccine sentiment, previously identified pro- and anti-vaccine arguments, and WN-specific arguments. RESULTS: Discourse was dynamic, increasing around specific events, such as outbreaks and following legal debates about vaccine mandates. We identified four themes: conspiracies, science, race and white innovation. The prominence of themes over time was relatively stable. Our manual coding identified levels of anti-vaccine sentiment that were much higher than found in the past on mainstream social media. Most anti-vaccine posts relied on common anti-vaccine tropes and not on WN conspiracy theories. Pro-vaccination posts, however, were supported by unique race-based arguments. CONCLUSION: We find a high volume of anti-vaccine sentiment among WN on Stormfront, but also identify unique pro-vaccine arguments that echo the group's racist ideology. PUBLIC HEALTH IMPLICATION: As with past health-related conspiracy theories, high levels of anti-vaccine sentiment in online far-right sociotechnical information systems could threaten public health, especially if it 'spills-over' to mainstream media. Many pro-vaccine arguments on the forum relied on racist, WN reasoning, thus preventing the authors from recommending the use of these unethical arguments in future public health communications.

The Framing of COVID-19 in Italian Media and Its Relationship with Community Mobility: A Mixed-Method Approach.

Media framing of epidemics was found to influence public perceptions and behaviors in experiments, yet no research has been conducted on real-world behaviors during public health crises. We examined the relationship between Italian news media coverage of COVID-19 and compliance with stay-at-home orders, which could impact the spread of epidemics. We used a computational method for framing analysis (ANTMN) and combined it with Google's Community Mobility data. A time-series analysis using vector autoregressive models showed that the Italian media used media frames that were largely congruent with ones used by journalists in other countries: A scientific frame focusing on symptoms and health effects, a containment frame focusing on attempts to ameliorate risks, and a social frame, focusing on political and social impact. The prominence of different media frames over time was associated with changes in Italians' mobility patterns. Specifically, we found that the social frame was associated with increased mobility, whereas the containment frame was associated with decreased mobility. The results demonstrate that the ways the news media discuss epidemics can influence changes in community mobility, above and beyond the effect of the number of deaths per day.