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.

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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.

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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.