title: How can I measure trends in certain words, like Twitter?
body: <p>I have newspaper articles' corpus by day. Each word in the corpus has a frequency count of being present that day. I have been toying with finding an algorithm that captures the break-away words, similar to the way Twitter measures Trends in people's tweets.</p>
  
  <p>For Instance, say the word 'recession' appears with the following frequency in the same group of newspapers:<br>
  Day 1 | recession | 456<br>
  Day 2 | recession | 2134<br>
  Day 3 | recession | 3678</p>
  
  <p>While 'europe'<br>
  Day 1 | europe | 67895<br>
  Day 2 | europe | 71999<br>
  Day 3 | europe | 73321</p>
  
  <p>I was thinking of taking the % growth per day and multiplying it by the log of the sum of frequencies. Then I would take the average to score and compare various words.</p>
  
  <p>In this case:<br>
  recession = (3.68*8.74+0.72*8.74)/2 = 19.23<br>
  europe = (0.06*12.27+0.02*12.27)/2 = 0.49</p>
  
  <p>Is there a better way to capture the explosive growth? I'm trying to mine the daily corpus to find terms that are more and more mentioned in a specific time period across time. PLEASE let me know if there is a better algorithm. I want to be able to find words with high non-constant acceleration. Maybe taking the second derivative would be more effective. Or maybe I'm making this way too complex and watched too much physics programming on the discovery channel. Let me know with a math example if possible Thanks!</p>