Misleading vs. Visual Rhetoric (10 minutes)
More than alphanumeric writing or speech, using visual rhetoric to communicate about quantity can create very, very misleading interpretations of data despite using information that is “true”.
This video is brief video (about 4 minutes) sums up some important things to look out for (mostly, size and intervals of scales on the axes and also missing data as well as contextual information needed to interpret):
How to spot a misleading graph – Lea Gaslowitz – YouTube
For your own data and any charts you might make, it will be important that you have a meaningful scale and interval for your axes AND that it is not misleading. Sometimes, that means you have to change the axis. This page explains how to do that in Excel: Change the display of chart axes (microsoft.com)
However, being misleading vs. making rhetorical choices are very different things. I want us to keep thinking about this, but first let’s get some thoughts down. What is misleading vs. rhetorical in making data visualizations like tables and charts?
Go to this Google Doc to list your thoughts on misleading and rhetorical choices in data visualization–just add thoughts to the first two boxes (we will do third box later on in class)
Here is the lesson plan on tables: April 7, 2022 Lesson Plan – Data and Writing Toward Social Change, Spring 2022 (cuny.edu)
Here is the lesson plan on charts: April 12, 2022 Lesson Plan – Data and Writing Toward Social Change, Spring 2022 (cuny.edu)
Non-Traditional Visualization (20-30 minutes)
Let’s talk more about some non-traditional data visualization techniques
In chapter 3 of Data Feminism, they talk a lot about the history of scholarship on best practices for data visualization and how some of them may inadvertently perform the “god trick” in presenting itself as all-knowing, totalizing, and neutral standpoints on a given set of data. That is, the “view from nowhere” that suggests all can be seen by viewing these specific lines, bars, etc.
Their argument is that, if you are not offering up inaccurate data that would be lying or offering an invalid interpretation (as is done with misleading graphs we saw in the video), then why not embrace emotion and embodiment that capture attention and interest?
Things like:
- Using color
- Using images
- Using motion
- Adjusting font type or size
- Tone and rhetoric of language used
- Using interaction
- Playing with size (again, if not misleading)
- Performance or audio
D’Ignazio and Klein call this “data visceralization” when writers use elements like the above to try to get people emotionally invested in the visualization while still offering valid and accurate interpretations of data. As they argue with the New York Times’ “gauge” for elections, such work also can help communicate non-intuitive information.
Don’t limit yourselves with being creative in how you can express quantitative information! If you want to write to get people to think and act, data visualization minimalism may actively undermine your goals. (highly recommend revisiting chapter 3 with data visualization particularly in mind).
Resources
Things you can use that are easy and free to do some of the stuff described above. Click around and see if anything looks interesting!
Flourish | Data Visualization & Storytelling
Online Diagram Software & Visual Solution | Lucidchart
Free Data Visualization Software | Tableau Public (we are going to do a tutorial on this later on in April!!)
Task
Browse these four data visualizations:
Caffeine in food and drink industry (scroll down for the larger graphic).
Acquisition strategies of tech companies
On Discord, go to # 4-14-2022 and post the following including this information:
- What visualization do you think is best and why?
- What above elements of non-traditional visualization methods were effective do you think? Why?
- Optional: Instead of making your own post, you can respond to someone else’s post if they are talking about the visualization you wanted to talk about. You can agree or disagree (respectfully) by extending their points with new information or adding a new element that they didn’t bring up.
Let’s go back to the Google Doc and fill in the third box and then answer this question:
What about non-traditional visualization techniques? Are they misleading? Are they rhetorical but not misleading? Something else?
Data Visualization Revision (10-20 minutes)
Take out your drawing from April 12. How might you use some techniques we looked over today to revise what you sketched out from April 12?
Try to add to your drawing or make a new drawing.
We will do an exhibit in class, so be ready to share!! Have fun with it, no one is expected here to be professional artists.
Data-Driven Argument Check in (10-15 minutes)
How is this going? Any questions? Don’t forget cover letter!
I also want to mention that you can still do things like the following in the cover letter:
- Ask me questions
- Add notes about parts you weren’t sure about but would work more on if you worked on a third draft
- Add notes about the ways in which you would have ideally done something like your data visualization, your analysis of your data, etc.
- Anything and everything that shows you thinking about your draft and even toward a third draft
Next Time
-Finish DDA second draft and submit by tonight
-Keep working on Campaign for Circulation, to include your proposal for that project due by April 28.
-Tuesday, April 26 has the last Response Post due. If signed up or if you need another post for minimum (2 for semester) or for grade boost (4 for semester), then don’t forget to do this by class time on Tuesday. Everyone else, you’ll comment by end of day Tuesday, April 26. Prompt and assignment instructions are up on Blackboard.
-Response Post on April 26 is about a reading on “rhetorical velocity” which will be an important concept for our Campaign for Circulation project. See assignment instructions to help you read this text as it is organized in an untraditional way.