The presentation needed to solve for three key challenges:

Complex subject matter. Some social psychology concepts like attitude change are deceptively complex. While social conformity might be simplified to “monkey see, monkey do,” understanding our research required an intuitive sense of more precise, dynamic models (i.e. Sherif and Sherif’s Social Judgement Theories on interpersonal influence).

Complex data. The main results were based on 1,962 participants’ responses to 7,560 dynamically generated charts, looking across 5 political policy topics, 6 different charts types, and multiple signals of political ideology. Much to the chagrin of “Reviewer #2,” our models were necessarily complex. While scatter plots and regression lines could straightforwardly show charts’ influences on participants’ attitudes, showing unexpected emergent effects like “polarization” meant capturing aggregate, relative changes between groups.

Politics are exhausting. No one wants to talk about politics at a dataviz conference. Even in 2023, the emotional landscape of political beliefs and polarization were distressing, precarious, and intrinsically divisive. And charged issues risk emotional barriers to learning.

Narrative. While social influence could be modeled as a complex network of causes and effects, our simple human brains prefer stories. Narratives can be a surprisingly effective way to convey complex causal influences (Sloman 2017, Niederdeppe 2013).

Motion. Change is easy to comprehend when you can see it happening. Animated visualizations can help audiences grasp dynamic systems more intuitively than static charts alone.

Fables are safe. Animals are cute. Following storytellers from George Orwell to Sesame Street, sensitive topics are more easily discussed in the animal kingdom. While divisions between “Democrats vs Republicans” are tired and painful, “Cats vs Dogs” are an even more recognizable rivalry, evoking similar loyalties, but with much lower stakes.

The talk followed the classic arc of research presentations: Motivating context and key concepts up front, create tension with a knowledge gap, then get to the science.

Motivation And Key Concepts

To motivate the study and cover background concepts, we started with a story about a controversial policy in the animal kingdom (“The Canadian Can Ban Plan”) and then introduced our characters: Jake the moderate dog, Jane the wise influencer (also a dog), and Tom the moderate cat. We used visual metaphor to cover attitude conformity in the model of Sherifs’ Social Judgement Theory: We posed Jake and Jane on an attitude scale, then literally shifted Jake toward Jane, as his attitude converged towards hers.

We used this same narrative world to introduce the stimuli charts and open questions for the research (e.g., “Could charts influence Jake like Jane did?”). And as the questions became more complex we built up more complex diagrams iteratively (i.e. adding more cats and dogs to represent the group dynamics).

Visualizing The Results

There were two important parts to showing the polarization dynamics.

First, the “attitude gap” chart shows the amount of polarization driven by each stimuli chart, encoded as the distance between the blue and red dots on each row.

This chart could have been a bar chart. Showing these values as left-aligned bars would even make the gap sizes easier to compare. However, given this dataset with such obviously wide gaps for the partisan charts, we chose a non-traditional approach to afford a more immediate gist read.

• We split the uncertainty distributions to either side and, even though they aren’t specifically tied to either party, we colored them blue and red to signal that the gap is indeed between Democrats and Republicans.
• The plots are also centered horizontally to avoid implying that one party was more susceptible to this influence than the other, a more subtle question not answered by this analysis.
• This also parallels the visual metaphor we used for showing influence on Jake and his animal friends.

While these choices preclude conventional affordances (e.g. an x axis scale), we consider them worthy tradeoffs for an easier gist read, making it unavoidably clear that partisan charts increased partisan polarization.

Second, pairing the divergence diagram with our friends from the animal kingdom created a natural way to teach audiences how to read the charts, in real time. We also show the same movement again after showing the results slide, to reinforce the impact of the specific partisan chart we studied.