The description of word clouds employed by Jeffrey Zeldman as the “mullets of the internet” made me laugh. I’ve never found them particular attractive to look at. That said, using tools like Wordle, Many Eyes and Voyant was fun and, like the Altmetrics doughnuts made the data in the otherwise eye-strainingly dull Excel spreadsheets much easier to get my head around, though I’m not sure how useful they are beyond getting a very general picture of a situation.
That said though, we used data collected from our altmetrics work in the last DITA lab and a few things were revealed to me. Firstly, using Altmetric I performed a keyword search for “Aotearoa” as I mentioned in my previous blogpost. When I looked at the results produced by Altmetric, it seemed that some of the journal articles/blog posts/Tweets etc it gathered did not contain the word Aotearoa, and it felt like the results were a bit random. However, using Voyant on the titles from the Altmetric data exported to Excel, resulted in the following word cloud:
with the word “Aotearoa” (as well as “Zealand”) showing very prominently, which lead me to realise that I probably dismissed my Altmetric results too quickly, and on further inspection they were more relevant than I thought – and the word cloud more than just a colourful mullet! (And yes, I did forget to include “stop words” which is why “and” and “of” appear so frequently – oops).
I also gathered Altmetric data using the keyword “Bicycle” and exported these to Voyant as well. This screenshot shows the kinds of information Voyant pulled out for me:
One of the most useful features is being able to select a word, in this case “helmets” from the corpus, and on the bottom right of the screen, the instances of this word being used are shown, surrounded by the context of the sentence (which can be expanded). This is useful if the word the researcher is looking for is more ambiguous than “helmets” or “Aotearoa”, and could perhaps be mentioned in a context irrelevant to the thing being studied. This more granular way of looking at the data ensures that the researcher is getting an accurate picture of how the words are being used in the text, with minimal effort.
I still can’t say I am convinced by the usefulness of the word cloud, or even 100% sold on text analysis when looked at in this quantitative way. I did my undergraduate degree in English literature, so I guess Franco Moretti’s concept of distant reading which employs graphical and quantitative visualisations of a text is a new one to me (though would have been REALLY helpful when writing those essays on Victorian literature!). But I was interested in Julie Meloni’s blogpost at the Chronicle of Higher Education regarding the use of word clouds for engaging students. I used to work in a youth library, and many of the teenagers I worked with were very interested in poetry and expressive language. “The Rose that Grew from Concrete” by Tupac Shakur was (perhaps unsurprisingly) one of the most popular books in the library. In a bid to get the kids to engage with how poems are written, I photocopied some of Tupac’s poetry and whited-out some of the more visceral words. The kids then had to imagine/guess/decide what words should be used where the spaces were. I just used Voyant on poems from “The Rose that Grew from Concrete”, and I think that this would’ve been a hit amongst all those emo teenagers at the library: