Recently I’ve been speaking with several folks (e.g. Megan Winget and Gene Golovshinsky) about how twitter is or might be important with respect to academic conferences. I’ve got some research coming up where I hope to look at this.
But in the meantime, I put this together:
A caveat: having slapped this together quickly, I’m not sure how the site will behave…I hope it is relatively solid.
I put the page up just because it seemed like a natural thing to do given the data that I’ve been collecting (relatively large amounts of twitter-generated info). I’m hoping that it might, even a little bit, encourage the conference attendees to think of twitter as they listen, chat, etc.
Jeff Dalton has a great post up about the New York Times‘ recent announcement: the paper has lauched data.nytimes.com. Currently the service offers 5k named (i.e. people) subject headings from the NYT news vocabulary. The headings are available as linked open data. More headings are on the way.
Handwringing (e.g. here, here and here) , maybe deserved, has been in abundance recently in the arena of print journalism. Finding/maintaining viable business models for high-quality reporting in environments where free information is readily available is a challenge.
I’ve been rooting for NYT in this struggle. In this respect I’m glad to see their release of data. Rather than leaning on the obvious and dubious advertising model or walled gardens, this strikes me as a gambit for a novel approach to attacking the problem of the papers’ value.
Can we (i.e. hackers of textual data) repurpose and add value to the excellent information compiled by the Times’ editors? Is there a viable business model for the Times that could emerge from releasing data, as opposed to closing it? It’s a creative response to a problem that is full of caricature. I hope we’ll take up the challenge.
Twitter hashtags are a great tool for improvised info organization–i.e. using software features to marshall information in ways that the feature designer didn’t think up (and made no pretense of thinking up). In particular, I’ve been thinking of hashtags as a hack to support collaborative IR. Need to research the size of Google Scholar’s index? Mark relevant resources with, say, #search.GSsize . Others interested in the same topic could add to the body of knowledge related to the search, and could follow the collected resources.
Of course this is what hashtags are for, so I’m not proposing anything very new here.
But this idea got me thinking of a few services that would support hashtag use for collaborative IR:
- Intelligent search for tags
- hashtag disambiguation.
Other services like recommendation also leap to mind.
By intelligent search, I’m thinking of a way to find tags that are relevant to a particular topic. hashtags.org/tags already collects tags. But as far as I know (please correct me if I’m wrong) existing hashtag search simply supports string matches. It’s difficult to find semantically useful tags. This would frustrate any kind of real collaborative use of them.
As for hashtag disambiguation, I simply mean trying to identify and separate different semantic uses of the same tag character string. The admirably ungoverned nature of hashtags naturally leads to collisions. For example #ir primarily yields tweets related to Iran; not what I had in mind.
Another example: I’m an amateur (VERY amateur) painter with a particular interest in paintings mediums. Too lazy to type #paintingMedium on my phone (I’m not alone in this, I see), I’m inclined to tag things with #medium, which tosses my lot in with scads of information on the TV show. These collisions aren’t a problem as I organize my own posts, but they would be if people wanted to search broadly for useful tags, jumping onto a tag in medias res.
What I’m suggesting is that it would be useful and interesting to tackle the complexity of hashtags in efforts to extend their utility. A first step here would be to analyze the text that accompanies them. But I suspect this wouldn’t be enough. Would we need to consider the social structure in which tags are embedded? I sense an opportunity here.