I took about 600 recent tweets with the hashtag #NHSbill and analysed them for word clusters using Latent Dirichlet Allocation (a hierarchical Bayesian technique). I've been thinking of building a tool that could (1) help identify discussion themes, (2) find related tweets, and (3) suggest tweeps that have similar or different views to your own.
This is a stab at step (1). The following collections of words are summaries of the discussion themes and, given that it's Twitter, associated tweeps (hope you don't mind the associations) and links.
#nhs @silv24 proud
disaster healthcare standing
talking professionals Lansley
week Andrew @ijgreener
500 GPs [involved in] commissioning groups
want clinical withdrawn
@rcplondon @djnicholl oppose
gen care pensions
r4 says Times
enough thrown out
What was frustrating was that @profchrisham kept popping in and out of the model as I was fiddling with the stop word list ('a', 'the' ,'and' etc.). Supportive voices for the bill were uncommon in the sample of tweets so I might go for a bigger sample. Like I said before, maybe I need to find a debate that is less, eh, one-sided, on Twitter.
Just finding discussion themes, step (1), can be a way of following the various threads in a Twitter hashtag live meeting - if could be done live.