The Northwestern Election Forecasts

We are a team of researchers at Northwestern University. Our mission is to enhance public understanding and trust in democratic processes by developing and assessing innovative election forecast visualizations. Our approach integrates complex data analysis, innovative design solutions, and comprehensive user studies to ensure that our visualizations are both informative and trustworthy. Through our interdisciplinary research, we aim to create clear, effective, and trustworthy visual tools that empower citizens to engage knowledgeably with electoral information.

Our reseach projects

Swaying the Public? link
Led by Yang et al., this project measured the impacts of election forecast visualizations on emotion, trust, and intention during the 2022 U.S. midterm elections. It was published IEEE Visualization and Visual Analytics Conference in 2023, and won a Best Paper Award.
forecast: https://forecasts.cs.northwestern.edu/2022-governors-elections

In Dice We Trust link
Led by Yang et al., this project used a sequence of simulated U.S. presidential elections, conducte three decision-making experiments, and provided recommendations of visual design(s) for maintaining people's trust in election forecasts. It was published at ACM CHI Conference on Human Factors in Computing Systems in 2024, and won a Best Paper Award.
forecast: https://forecasts.cs.northwestern.edu/2023-hypothetical-elections

A Design Chronicle link
Led by Yang et al., this scholarly article introduces out design process for the 2022 governors election forecasts and summarizes the design knowledge learned. It was published at IEEE Visualization and Visual Analytics Conference in 2024.
demo 1: https://forecasts.cs.northwestern.edu/2022-initial-prototypes
demo 2: https://forecasts.cs.northwestern.edu/2022-interview-prototypes

Election Sausage link Mandi Cai and Matthew Kay
Led by Cai and Kay, this project classified the U.S. vote counting process dashboards, and articulated the challenges faced by those data journalists. It was published at ACM CHI Conference on Human Factors in Computing Systems in 2024, and won a Best Paper Honorable Mention.

Our interdisciplinary team

*alphabetical order

Core members^*

• Mandi Cai, Ph.D. Student in Technology and Social Behavior
• Matthew Kay, Professor in Computer Science and Communication
• Fumeng Yang, Postdoctoral Researcher in Computer Science (now faculty at University of Maryland, College Park)

Core collaborators^*

• Lawrence A. Birnbaum, Professor in Computer Science
• Andrew Crotty, Professor in Computer Science
• Nicholas Diakopoulos, Professor in Communication Studies and Computer Science
• Chloe Mortenson, Ph.D. Student in Technology and Social Behavior
• Erik C. Nisbet, Professor in Policy Analysis and Communication
• Jennie M. Rogers, Professor in Computer Science

Collaborators^*

• Hoda Fakhari, Ph.D. Student in Technology and Social Behavior
• Steve Franconeri, Professor in Psychology, Marketing, Design, and Computer Science
• Jessica Hullman, Professor in Computer Science
• Ayse D. Lokmanoglu, Postdoctoral Researcher in Communication (now faculty at Boston University)

Media coverage

Building Informative and Trustworthy Election Forecasts by Michelle Mohney

Partner labs

Midwest Uncertainty Collective (Kay and Hullman), Center for Communication & Public Policy (Nisbet), Computational Journalism Lab (Diakopoulos), Future Intelligent Graphical Experience (Yang), Visual Thinking Lab (Franconeri)