Transcript of the #cilip2 Twitter hastag

Despite a widespread network failure that seemed to affect quite a few universities, I finally managed to pick up all of the #cilip2 tweets from today’s event:
Whenever I get a spare half-an-hour, I’ll do some analysis of the tweets. If anyone want a tab separated version of the data, you can grab it from here.

Tweet Clouds

I have a confession to make — I grew bored of Twitter after a couple of days.
However, I felt obliged to keep on Twittering something… anything… so I hooked our OPAC into the feed instead. Every 5 minutes, a bit of code checks to see what the most popular keyword(s) used on our OPAC has been recently and, if it’s different to the last run, it fires it off to Twitter. I was so lazy, I didn’t even bother filtering out stopwords.
The result is an eclectic mix of words that encapsulate our student’s usage of the library catalogue — little snapshots of what was important to a bunch of students (or perhaps one particular determined student). Topics meander semi-randomly, occasionally repeating at unusual intervals.
Sometimes, there’s not a single popular keyword, but several. Sometimes the multiple words make sense, other times they create weird phrases…

  • british genetics music
  • angina attachment theatre
  • education picasso sex
  • rape skills study

Anyway, a few days ago I spotted Tweet Clouds and decided to see what it made of my feed…
…and here’s a cloud I made back in December 2006
I must admit, I feel kinda guilty that I ate up 23 minutes of CPU time on the Tweet Cloud site :-S

Twittering the OPAC

I noticed that a few other people have been blogging about how they’re using Twitter within their own library, so here’s how I’ve been (ab)using my Twitter feed…
Last year, I set up some data feeds from the library that our students could hook into for their projects. This data includes a file listing the keywords used in the last 50 searches on the OPAC and their frequency.
The file is also used to generate this keyword cloud (which ignores words with a frequency of only 1).
Anyway, after getting bored with manually updating Twitter myself, I decided to hook it into the keyword file. Every few minutes, an automated script checks the file for the most frequently used keyword(s) from recent searches, and sends it off to Twitter.
I’m not sure if it really serves any useful purpose, but it’s kinda fun to see a top-slice of what our users are searching for. Occasionally the keywords link together to make weird statements…

…so, the next time Twitter crashes, you can probably blame me 😀