Given a company or personal name, what’s a quick way of generating meaningful tags around what it’s publicly known for, or associated with?
Over the last couple of weeks or so, I’ve been doodling around a few ideas with Miguel Andres-Clavera from the JWT (London) Innovation Lab looking for novel ways of working out how brands and companies seem to be positioned by virtue of their social media followers, as well as their press mentions.
Here’s a quick review of one of those doodles: looking up tags associated with Guardian news articles that mention a particular search term (such as a company, or personal name) as a way of getting a crude snapshot of how the Guardian ‘positions’ that referent in its news articles.
It’s been some time since I played with the Guardian Platform API, but the API explorer makes it pretty easy to automatically generate some (the Python library for the Guardian Platform API appears to have rotted somewhat with various updates made to the API after its initial public testing period).
Here’s a snapshot over recent articles mentioning “The Open University” (bipartite article-tag graph):
Here’s a view of the co-occurrence tag graph:
The code is available as a Gist: Guardian Platform API Tag Grapher
As with many of my OUseful tools and techniques, this view over the data is intended to be used as a sensemaking tool as much as anything. In this case, the aim is to help folk get an idea of how, for example, “The Open University” is emergently positioned in the context of Guardian articles. As with the other ‘discovering social media positioning’ techniques I’m working on, I see the approach useful not so much for reporting, but more as a way of helping us understand how communities position brands/companies etc relative to each other, or relative to particular ideas/concepts.
It’s maybe also worth noting that the Guardian Platform article tag positioning view described above makes use of curated metadata published by the Guardian as the basis of the map. (I also tried running full text articles through the Reuters OpenCalais service, and extracting entity data (‘implicit metadata’) that way, but the results were generally a bit cluttered. (I think I’d need to clean the article text a little first before passing it to the OpenCalais service.)) That is, we draw on the ‘expert’ tagging applied to the articles, and whatever sense is made of the article during the tagging process, to construct our own sensemaking view over a wider set of articles that all refer to the topic of interest.
PS would anyone from the Guardian care to comment on the process by which tags are applied to articles?
PPS a couple more… here’s how the Guardian position JISC recently…
And here’s how “student fees” has recently been positioned:
Hmmm…
