Word clouds: “mullets of the internet”? What would Tupac say?

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:

BicycleVoyantOne 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:



Using Altmetrics to measure societal impact

The value of using alternative metrics to collect evidence of impact for a scholarly work appeals to me, because it opens up the notion that the “general public” are thinking and reading also, not just academics.   I am involved with lots of communities that I guess you’d call grassroots: activist and music communities particularly, which are not connected to universities, but are often political in nature (feminist, post colonial and queer theory contributing significantly).  The open and cheap dissemination of information and ideas is important to these networks and while zines have long played a big part in this, often introducing the theories of seminal thinkers (see for example, this zine, Judy!, a tongue in cheek zine about Judith Butler, recently digitised by QZAP – the Queer Zine Archive Project), the internet has obviously by and large taken over (though zines still live on!) and these same communities continue to share theory on Tumblr, Twitter, Facebook etc.  Gathering evidence that scholarly works are being discussed outside of the ivory tower I think not only is gratifying for the scholar, but also provides an important channel of feedback as academics are able to see the context in which their work is being used.

Working in an academic library I hear a lot about the Research Evaluation Framework (REF), which is basically how funding decisions are made for research in Universities.   As said on the Ref home page: “The assessment provides accountability for public investment in research and produces evidence of the benefits of this investment”. Which I guess is a fancier and more money-focused way of saying what I said above.   Clearly, altmetrics will play a significant part in proving the worth of areas of research, particularly with the shift to Open Access that is also being hustled along by the REF.

As we discussed in our DITA lecture, altmetrics cannot be relied upon for the whole picture.  Tools such as Altmetric rely on documents having DOIs, and also due to the ever-shifting nature of social media, results are not stable, they will only ever provide a snapshot for a moment in time.  Five minutes later, things could be different.  Not only that, but the way that we share things on social media can often be flippant and superficial; i.e. just because I share a link doesn’t mean I’ve really read it.  However, as Ernesto Priego points out on the Altmetric blog, using altmetrics often (but not always) means you can pinpoint data such as the geolocation of the person sharing the link, which can give added weight to the significance of the share.

AltmetricsUsing Altmetric Explorer last week was an interesting experience.  I was a bit frustrated that the keyword search didn’t seem particularly accurate, for example, I wanted to search for mentions of “Aotearoa”, which is the Maori word for “New Zealand”, as I thought it would cut out the chances of picking up articles about “Zealand” in Denmark. However, despite the uniqueness of the name, some of the articles returned did not contain the word, or even have anything to do with NZ at all.  I couldn’t get to the bottom of this. Also I noticed that mostly Science-based journals were being discovered, but I guess this is probably due to the these journals having a higher proportion of DOIs over journals in the humanities and literature, which is probably where I was more likely to find the topics I was interested in.  One thing I wondered about what whether there was any kind of correlation between how “populist” the article topic was, and what kind of social media was used to share it, e.g. perhaps including Pintrest and Tumblr in my search scope would reveal something rather different than if I stuck to news sites and blogs…however, it was difficult for me to judge that from the results I received (partly because the notion of what’s “populist” is subjective I think).

Looking at the Altmetric “doughnuts” was far more pleasing and easy to take in at a glance than using Excel spreadsheets, and I will definitely be going back to this tool and hopefully will be able to get more out of it with practice.