Transcript
Speaker: Welcome back to the Policy Viz Podcast. I'm your host, Jon Schwabisch. It's an intervening week here, folks. We've got another one of the My Favorite Graphs series here on the podcast. So this is the second time I'm doing it.
Speaker: I hope you find it interesting. These are just the graphs that remember used to teach from stick in my head, not doing them in any particular order, just as I kind of feel like it and what comes up in top of mind.
Speaker: And for this week's episode, I'm going to talk about a 2016 piece from the New York Times, Wilson Andrews, Rebecca Lye, Irina Mikalushin, I'm not sure I pronounced her name right, and Alicia Parlapiano. This piece came out in July of 2016. This was right after the the nomination speeches by donald trump and hillary clinton at the respective republican and democratic uh conventions um this is qualitative visualization and they're not a lot of those out there and this one sticks with me because it's qualitative which is kind of rare b it's a really really impressive work even though it's it's at its core kind of like very straightforward
Speaker: And um three is something that I have actually used in my work. Okay, so what is the piece? ah I'll put the link in the show notes, but the title is Stronger Together and I Am Your Voice, How the Nominees' Convention Speeches Compare. And basically...
Speaker: there are a series of these visualizations. It's essentially, ah you can think of it as like a zoomed out version of each speech. So Donald Trump's version on the left, Hillary Clinton's speech on the right. So you think of like a Word document zoomed all the way out. So you can see the thumbnail images of all the pages and then phrases highlighted. In the first set of graphs, the phrases are highlighted in a pink color showing what's wrong with the country and ah phrases that are highlighted in green or shaded in green are what's good about the country. And what you can see right away in the first set of images is that Donald Trump's ah speech, nomination speech, is...
Speaker: is more has more phrases, more words about what's wrong with the country than Hillary Clinton's, which has a little bit more of ah a balanced approach, more green, I think, than than pink. I think Trump only has like maybe three or four, two or three, two, three, four phrases in there that are what's good about the country, which um probably doesn't really surprise most people who who have listened to these these two politicians. But what I love about this is that We take qualitative data and we show or they show the overarching trend, the overarching summary. And it's not like they're summing up the negative words, because how do you sum up negative words and then create a bar chart out of that? Right. You'd have to sort of get into each. you know, a sentence can have mixed parts of it, right? So you have to get into the semantics of each sentence. And so I i don't really see how showing a bar chart of the negative words versus the positive words actually works as well as this, where I can actually see the entire speech and see it fairly clearly shaded. And then, of course, because the Times has, you know, it's a web page, so it's
Speaker: you know virtually infinite scroll, they have a few of the quotes layered below. So you can actually sort of see how you know, what they kind of identified in in this case, in the first graphs of these two categories. And I've used this in my in my own approach and some of my own work. um I've done it very simply where I've literally done what I described, which is like a Word document. I highlight the words or the phrases or the sentences for the different categories that I'm looking at. So, you know, positive, negative, neutral or negative.
Speaker: ah Yes, no, not sure. um Highlighted those and then literally just zoomed all the way out and took a screenshot of it. And that was the easy way to do it. I know there's an R package called GG page, which I've never used um that can do this. There's also the ability to do this on the flourish data visualization tool where you can put in your your text and then set which words or phrases you want highlighted. So that's ah another way to sort of make these practically. um But I just really like this because it's you get to see the patterns. And i think for especially for qualitative data, we're not always looking for an exact count. I mean, that's sort of the richness of the qualitative data is that we're not always looking for a rich count of how many words or phrases are about, you know, this thing or that thing or the other thing. But we're looking for the overarching pattern, the overarching trend ah in in the data. So um I just want to also show that there were or talk about that there were I think there were two other sets of these they had as you scroll down another set again for Trump on the left part of the screen and Clinton on the right highlighted in yellow promises vision and policy proposals. So that's just like a series of yellow highlights and then they did it also for the vice presidential candidates. So Mike Pence, ah Tim Kaine, where they had purple for praise for their running mates. And then they had, I think at the ah towards the end here, I'm just scrolling through, um ah criticizing each other. um Again, that same approach you can think of as just a zoomed out version and then just highlight it. And I've seen...
Speaker: different versions of this, over time. Um, there was one, I believe in the Washington post, uh, for George Santos, highlighting changes in his website as, uh, the sort of,
Speaker: the the this is a congressman from New York who was constantly lying about his credentials. So they sort of are highlighting all of the changes and when they occurred and for what reason. um Again, I really like this approach. I think it's very clever for qualitative data visualization. But let's hear from Alicia who sent in a little voice note about her recollections of this project.
Speaker: Okay, um I don't remember a lot about this project. It was more than 10 years ago at this point, but I will do my best. um Often for big speeches by presidents or candidates, we would visualize the most common words that they use to try to tease out the differences between like a Democrat and Republican speech or sort of summarize the issues the speaker was focusing on. um but But this approach sort of compares and all these speeches at sort of a higher thematic level than than just straight word counts. um
Speaker: but Looking at it now, I'm not actually remembering, but it's possible that we were struck by the negativity in Trump's speech that was sort of like atypical for a presidential candidate.
Speaker: um He had spent a lot of time in his speech talking about crime and violence and sort of what was wrong with the country. And so I would imagine that we sort of use that as our frame or kind of at the starting point for this piece. um And you can see that Hillary Clinton also pointed out some problems that the country was facing, but she didn't dwell on those for nearly as long. And she included a lot of examples about what was good about the country as well.
Speaker: um I dug around a little bit to find the working documents for this project and I did find a Google Doc where we basically highlighted the text of all of the speeches with colors that matched exactly what you see in the final piece. And I think KK Lai was the one who built the page and she pulled in the categorization by sticking little prefixes in the Google Doc using Archie ML which was is the markup language that Archie say, the New York Times graphics director, had recently created and what we still use today.
Speaker: And then she rendered the text into these tiny images that sort of mimic the look of actual pages. So the final result is not really that different than what we were working with behind the scenes. um And i think the result is really fun. And it sort of leaves an impression that something like bar charts breaking down the speeches would not have given.
Speaker: Thanks, Alicia. Appreciate you sending that in. um Yeah, I mean, i I, you know, I think she sort of like underplays ah personally, I think she underplays how how valuable this approach is. I think this is one of the first times that I saw this approach of of coloring phrases. And I think it's something that we can all do, right? It's pretty straightforward to do. And I hate to talk about AI in every episode, but if you think about how we can do this now, the AI tools are making this sort of thing even easier in terms of the analysis, right, to identify phrases. sentences, phrases, paragraphs that fall into different categories. It makes it even easier. And then to create this, you know, really doesn't take that much. So I think if you're a qualitative researcher or a qualitative data visualization communicator, I would definitely put this approach in your toolbox. um
Speaker: once you've analyzed the data, to give people the full view of the transcript of the focus group or the interview of the speech so that they can see, oh yeah, okay, so so this person talked about this thing a lot. so And then give them maybe a couple of example quotes. So instead of just showing a single quote, which I think a lot of people...
Speaker: worry about presenting because it's just that single quote. It's just that, you know, maybe the person cherry picked it or maybe it's not really, you know, the the entire, you know, a perspective of the person speaking. You can sort of have both here. You can show how this person actually talked about, you know, this topic or use this sentiment or this phrasing across this entire, again, presentation, interview, focus group, speech, whatever it might be. So coloring phrases is one of the techniques that I throw in my qualitative data viz toolkit. And I think this is just a a great example of it. So thanks to Alicia. Thanks for the times. And I hope you found this episode of my favorite graphs useful.
Speaker: Okay, that's all i've got. Thanks for tuning into the policy of this podcast. And I will talk to you next week.
