In that case I would do it by number of submissions (posts) + number of comments for each period (3 months I think, looking at your graphs?). I would normalised all quarters to the last complete quarter.

So if say in q1 2011 there were 1m posts + comments, and in q1 2007 10k, multiply the count for q1 2007 by 100.

It would be truly great to have this normalisation optional, e.g option to normalise by comment+post count and maybe also normalise by number of users (could be an interesting comparison). And perhaps have them super-imposed on each other!

Coincidentally, I was also working on an HN Trends app a couple weeks ago (though I got distracted and haven't finished), and mine is normalized (by number of comments). For example: http://hn-trends.heroku.com/trends?q=scala%2Cclojure

Your app looks a lot better, though! (And my dataset ends around Sept. of last year.)

Divide by the weighted average change in the number of hits for the 100 most common English words? Basically, add up all the uses of "and," "or," "a," "the," etc. in each period. If Q3 2009 has 4X the sum of those words as Q1 2007, then divide all Q3 2009 results by 4.