When the Visualization Eclipses The Data...

In his 2003 novel Pattern Recognition, William Gibson created a character named Cayce Pollard with an unusual psychosomatic affliction: She was allergic to brands. Even the logos on clothing were enough to make her skin crawl, but her worst reactions were triggered by the Michelin Tire mascot, Bibendum.

Although it’s mildly satirical, I can relate to this condition, since I have a similar visceral reaction to word clouds, especially those produced as data visualization for stories.

If you are fortunate enough to have no idea what a word cloud is, here is some background. A word cloud represents word usage in a document by resizing individual words in said document proportionally to how frequently they are used, and then jumbling them into some vaguely artistic arrangement. This technique first originated online in the 1990s as tag clouds (famously described as “the mullets of the Internet“), which were used to display the popularity of keywords in bookmarks.

More recently, a site named Wordle has made it radically simpler to generate such word clouds, ensuring their accelerated use as filler visualization, much to my personal pain.

So what’s so wrong with word clouds, anyway? To understand that, it helps to understand the principles we strive for in data journalism. At The New York Times, we strongly believe that visualization is reporting, with many of the same elements that would make a traditional story effective: a narrative that pares away extraneous information to find a story in the data; context to help the reader understand the basics of the subject; interviewing the data to find its flaws and be sure of our conclusions. Prettiness is a bonus; if it obliterates the ability to read the story of the visualization, it’s not worth adding some wild new visualization style or strange interface.

Of course, word clouds throw all these principles out the window. Here’s an example to illustrate. About six months ago, I had the privilege of giving a talk about how we visualized civilian deaths in the WikiLeaks War Logs at a meeting of the New York City Hacks/Hackers. I wanted my talk to be more than “look what I did!” but also to touch on some key principles of good data journalism. What better way to illustrate these principles than with a foil, a Goofus to my Gallant?

And I found one: the word cloud. Please compare these two visualizations — derived from the same data set — and the differences should be apparent:

I’m sorry to harp on Fast Company in particular here, since I’ve seen this pattern across many news organizations: reporters sidestepping their limited knowledge of the subject material by peering for patterns in a word cloud — like reading tea leaves at the bottom of a cup. What you’re left with is a shoddy visualization that fails all the principles I hold dear.

Every time I see a word cloud presented as insight, I die a little inside.

For starters, word clouds support only the crudest sorts of textual analysis, much like figuring out a protein by getting a count only of its amino acids. This can be wildly misleading; I created a word cloud of Tea Party feelings about Obama, and the two largest words were implausibly “like” and “policy,” mainly because the importuned word “don’t” was automatically excluded. (Fair enough: Such stopwords would otherwise dominate the word clouds.) A phrase or thematic analysis would reach more accurate conclusions. When looking at the word cloud of the War Logs, does the equal sizing of the words “car” and “blast” indicate a large number of reports about car bombs or just many reports about cars or explosions? How do I compare the relative frequency of lesser-used words? Also, doesn’t focusing on the occurrence of specific words instead of concepts or themes miss the fact that different reports about truck bombs might be use the words “truck,” “vehicle,” or even “bongo” (since the Kia Bongo is very popular in Iraq)?

Of course, the biggest problem with word clouds is that they are often applied to situations where textual analysis is not appropriate. One could argue that word clouds make sense when the point is to specifically analyze word usage (though I’d still suggest alternatives), but it’s ludicrous to make sense of a complex topic like the Iraq War by looking only at the words used to describe the events. Don’t confuse signifiers with what they signify.

And what about the readers? Word clouds leave them to figure out the context of the data by themselves. How is the reader to know from this word cloud that LN is a “Local National” or COP is “Combat Outpost” (and not a police officer)? Most interesting data requires some form of translation or explanation to bring the reader quickly up to speed, word clouds provide nothing in that regard.

Visualization is reporting, with many of the same elements that would make a traditional story effective.

Furthermore, where is the narrative? For our visualization, we chose to focus on one narrative out of the many within the Iraq War Logs, and we displayed the data to make that clear. Word clouds, on the other hand, require the reader to squint at them like stereograms until a narrative pops into place. In this case, you can figure out that the Iraq occupation involved a lot of IEDs and explosions. Which is likely news to nobody.

As an example of how this might lead the reader astray, we initially thought we saw surprising and dramatic rise in sectarian violence after the Surge, because of the word “sect” was appearing in many more reports. We soon figured out that what we were seeing had less to do with violence levels and more to do with bureaucracy: the adoption of new Army requirements requiring the reporting of the sect of detainees. Of course, the horrific violence we visualized in Baghdad was sectarian, but this was not something indicated in the text of the reports at the time. If we had visualized the violence in Baghdad as a series of word clouds for each year, we might have thought that the violence was not sectarian at all.

In conclusion: Every time I see a word cloud presented as insight, I die a little inside. Hopefully, by now, you can understand why. But if you are still sadistically inclined enough to make a word cloud of this piece, don’t worry. I’ve got you covered.

This is an insightful and rather shrewd criticism of word clouds, and I think it applies to much of the infographic, data-visualizaion obsessed tech culture we live in.

I find myself fascinated by many of the new and innovative ways to graphically represent data. Yet, as Jacob Harris points out, many of these sleek new techniques (if they don't miss the point entirely) strip supposedly core ideas from the very context that lend them meaning... and we are left with a aesthetically pleasing series of pretty graphs and pie charts that convey very little actual information (see my post on the Infographic Idiom).

And even though CNN, Fox and other news networks are now embracing new visualization tools, tag clouds are ultimately useless measures of political sentiment, because concepts themselves really cannot be reduced to their most elemental articulation; in a word.

TED | Ads Worth Spreading

Here are the 10 winners of our first Ads Worth Spreading competition. With this competition, we're seeking to reverse the trend of online ads being aggressively forced on users. We want to nurture ads so good you choose to watch. On TED.com, ads run after our talks, not before. This means they can run longer than the TV-standard 30 seconds. And that's the key! In 2-3 minutes, there's enough time to really tell a story, share an idea, make an authentic human connection, become unforgettable. Instead of ambush, they offer pleasurable, intelligent engagement. We invite you to view, comment, rate -- and share!
via ted.com

Media Keyword Trends in Google's "Books Ngram Viewer"

Google unveiled it's new Books Ngram Viewer last week, which tracks words indexed by Google Scholar, allowing any user to potentially trace cultural trends through archived literature.  The above n-gram tracks the prevalence of the words "television," "radio," "newspaper," and "Internet," with relatively predictable results as media has morphed throughout the past century.

Though the data trends are of course limited to Google's own "canon" (of indexed books), the results that CNET calls A Time Machine For Wordplay are utterly fascinating. Profound shifts in American philosophy over the past two centuries can be traced to the prevalence of usage of the words "Liberty" vs. "Freedom," which I found in Alexis Madrigal's article in The Atlantic.


Also amusing is the rise of Vampires, Werewolves and Zombies in recent Literature.


And the Smartest Site on the Internet Is...

Mims's Bits

Google now lets you filter sites by "reading level".

The internet used to be full of highbrow reading material, until broadband penetration exploded and everyone with a credit card managed to find his or her way onto the web. Finding your way back to the rarefied air that used to suffuse the 'net can be a slog, so Google has a new way to help you out: You can now sort sites by reading level.

(For those of you following along at home, under Google's 'advanced' search, simply switch on this option by hitting the dropdown next to "Reading level.")

The results are fascinating. Searching for any term, no matter how mundane, and then hitting the "advanced" link at the top strips away all the spam, random blogs and all the rest of the claptrap from the advertisers, hucksters and mouthbreathers.

This is only one of the varieties of elitism enabled by the new feature, which was created by statistically analyzing papers from Google Scholar and school teacher-rated webpages that are then compared to all the other sites in Google's index.

As pioneered by Adrien Chen of Gawker, by far the most interesting application of the tool is its ability to rate the overall level of material on any given site, simply by dropping site: [domain.com] into the search box.

By this measure, the hallowed halls of the publication you're reading now fare pretty well:

Not quite as well as some sites that share our audience:

But certainly better than certain other, decidedly middlebrow, publications:

It's when you turn to the scientific journals that the competition really heats up:

And the battle between traditional and open access publishing models takes on a new dimension:

(Just for reference, Here's how MIT itself performs)

And, much as I'm loathe to admit it, the smartest site on the Internet is...

Meanwhile, excluding sites aimed at children, here's the dumbest:

-via technology review