Storytelling with data visualization is still very much in its “Wild West” phase, with journalism outlets blazing new paths in exploring the burgeoning craft of integrating the testimony of data together with compelling narrative. Leaders such as The News York Times create impressive data-driven presentations like 512 Paths to the White House (seen above) that weave complex information into a palatable presentation. But as I look out at the kinds of meetings where data visualizers converge, like Eyeo, Tapestry, OpenVis, and the infographics summit Malofiej, I realize there’s a whole lot of inspiration out there, and some damn fine examples of great work, but I still find it hard to get a sense of direction — which way is West, which way to the promised land?
And it occurred to me: We need a science of data-visualization storytelling. We need some direction. We need to know what makes a data story “work”. And what does a data story that “works” even mean?
Examples abound, and while we have theories for color use, visual salience and perception, and graph design that suggest how to depict data efficiently, we still don’t know, with any particular scientific rigor, which are better stories. At the Tapestry conference, where I attended, journalists such as Jonathan Corum, Hannah Fairfield, and Cheryl Phillips whipped out a staggering variety of examples in their presentations. Jonathan, in his keynote, talked about “A History of the Detainee Population” an interactive NYT graphic (partially excerpted below) depicting how Guantanamo prisoners have, over time, slowly been moved back to their country of origin. I would say that the presentation is effective. I “got” the message. But I also realize that, because the visualization is animated, it’s difficult to see the overall trend over time — to compare one year to the next. There are different ways to tell this story, some of which may be more effective than others for a range of storytelling goals.
Critical blogs such as The Why Axis and Graphic Sociology have arisen to try to fill the gap of understanding what works and what doesn’t. And research on visualization rhetoric has tried to situate narrative data visualization in terms of the rhetorical techniques authors may use to convey their story. Useful as these efforts are in their thick description and critical analysis, and for increasing visual literacy, they don’t go far enough toward building predictive theories of how data-visualization stories are “read” by the audience at large.
Corum, a graphics editor at NYT, has a descriptive framework to explain his design process and decisions. It describes the tensions between interactivity and story, between oversimplification and overwhelming detail, and between exploration and decoration. Other axes of design include elements such as focus versus depth and the author versus the audience. Author and educator Alberto Cairo exhibits similar sets of design dimensions in his book, “The Functional Art“, which start to trace the features along which data-visualization stories can vary (recreated below).
Such descriptions are a great starting point, but to make further progress on interactive data storytelling we need to know which of the many experiments happening out in the wild are having their desired effect on readers. Design decisions like how and where annotations are placed on a visualization, how the story is structured across the canvas and over time, the graphical style including things like visual embellishments and novelties, as well as data mapping and aggregation can all have consequences on how the audience perceives the story. How does the effect on the audience change when modulating these various design dimensions? A science of data-visualization storytelling should seek to answer that question.
But still the question looms: What does a data story that “works” even mean? While efficiency and parsimony of visual representation may still be important in some contexts, I believe online storytelling demands something else. What effects on the audience should we measure? As data visualization researcher Robert Kosara writes in his forthcoming IEEE Computer article on the subject, “there are no clearly defined metrics or evaluation methods … Developing these will require the definition of, and agreement on, goals: what do we expect stories to achieve, and how do we measure it?”
There are some hints in recent research in information visualization for how we might evaluate visualizations that communicate or present information. We might for instance ask questions about how effectively a message is acquired by the audience: Did they learn it faster or better? Was is memorable, or did they forget it 5 minutes, 5 hours, or 5 weeks later? We might ask whether the data story spurred any personal insights or questions, and to what degree users were “engaged” with the presentation. Engaged here could mean clicks and hovers of the mouse on the visualization, how often widgets and filters for the presentation where touched, or even whether users shared or conversed around the visualization. We might ask if users felt they understood the context of the data and if they felt confident in their interpretation of the story: Did they feel they could make an informed decision on some issue based on the presentation? Credibility being an important attribute for news outlets, we might wonder whether some data story presentations are more trustworthy than others. In some contexts a presentation that is persuasive is the most important factor. Finally, since some of the best stories are those that evoke emotional responses, we might ask how to do the same with data stories.
Measuring some of these factors is as straightforward as instrumenting the presentations themselves to know where users moved their mouse, clicked, or shared. There are a variety of remote usability testing services that can already help with that. Measuring other factors might require writing and attaching survey questions to ask users about their perceptions of the experience. While the best graphics departments do a fair bit of internal iteration and testing it would be interesting to see what they could learn by setting up experiments that varied their designs minutely to see how that affected the audience along any of the dimensions delineated above. More collaboration between industry and academia could accelerate this process of building knowledge of the impact of data stories on the audience.
I’m not arguing that the creativity and boundary-pushing in data-visualization storytelling should cease. It’s inspiring looking at the range of visual stories that artists and illustrators produce. And sometimes all you really want is an amuse yeux — a little bit of visual amusement. Let’s not get rid of that. But I do think we’re at an inflection point where we know enough of the design dimensions to start building models of how to reliably know what story designs achieve certain goals for different kinds of story, audience, data, and context. We stand only to be able to further amplify the impact of such stories by studying them more systematically.
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Comment Readers Want Relevance!
A couple years ago now I wrote a paper about the quality of comments on online news stories. For the paper I surveyed a number of commenters on sacbee.com about their commenting experience on that site. One of the aspects of the experience that users complained about was that comments were often off-topic: that comments weren’t germane, or relevant, to the conversation or to the article to which they were attached. This isn’t surprising, right? If you’ve ever read into an online comment thread you know there’s a lot of irrelevant things that people are posting.
It stands to reason then that if we can make news comments more relevant then people might come away more satisfied from the online commenting experience; that they might be more apt to read and find and learn new things if the signal to noise ratio was a bit higher. The point of my post here is to show you that there’s a straightforward and easy-to-implement way to provide this relevance that coincides with both users’ and editors notions of “quality comments”.
I collected data in July via the New York Times API, including 370 articles and 76,086 comments oriented around the topic of climate change. More specifically I searched for articles containing the phrase “climate change” and then collected all articles which had comments (since not all NYT articles have comments). For each comment I also had a number of pieces of metadata, including: (1) the number of times the comment was “recommended” by someone upvoting it, and (2) whether the comment was an “editor’s selection”. Both of these ratings indicate “quality”; one from the users’ point of view and the other from the editors’. And both of these ratings in fact correlate with a simple measure of relevance as I’ll describe next.
In the dataset I collected I also had the full text of both the comments and the articles. Using some basic IR ninjitsu I then normalized the text, stop-worded it (using NLTK), and stemmed the words using the Porter stemming algorithm. This leaves us with cleaner, less noisy text to work with. I then computed relevance between each comment and its parent article by taking the dot product (cosine distance) of unigram feature vectors of tf-idf scores. For the sake of the tf-idf scores, each comment was considered a document, and only unigrams that occurred at least 10 times in the dataset were considered in the feature vectors (again to reduce noise). The outcome of this process is that for each comment-article pair I now had a score (between 0 and 1) representing similarity in the words used in the comment and those used in the article. So a score of 1 would indicate that the comment and article were using identical vocabulary whereas a score of 0 would indicate that the comment and article used no words in common.
So, what’s interesting is that this simple-to-compute metric for relevance is highly correlated to the recommendation score and editor’s selection ratings mentioned above. The following graph shows the average comment to article similarity score over each recommendation score up to 50 (red dots), and a moving average trend line (blue).
As you get into the higher recommendation scores there’s more variance because it’s averaging less values. But you can see a clear trend that as the number of recommendation ratings increases so too does the average comment to article similarity. In statistical terms, Pearson’s correlation is r=0.58 (p < .001). There’s actually a fair amount of variance around each of those means though, and the next graph shows the distribution of similarity values for each recommendation score. If you turn your head side-ways each column is a histogram of the similarity values.
We can also look at the relationship between comment to article similarity in terms of editors’ selections, certain comments that have been elevated in the user interface by editors. The average similarity for comments that are not editors’ selections is 0.091 (N=73,723) whereas for comments that are editors’ selections the average is 0.118 (N=2363). A t-test between these distributions indicates that the difference in means is statistically significant (p < .0001). So what we learn from this is that editors’ criteria for selecting comments also correlates to the similarity in language used between the comment and article.
The implications of these findings are relatively straightforward. A simple metric of similarity (or relevance) correlates well to notions of “recommendation” and editorial selection. This metric could be surfaced in a commenting system user interface to allow users to rank comments based on how similar they are to an article, without having to wait for recommendation scores or editorial selections. In the future I’d like to look into ways to assess how predicative such metrics are in terms of recommendation scores, as well as try out different metrics of similarity, like KL divergence.