So, we can do some relatively intelligent diffing between python dicts (aka hash maps). We should implement a way to generate a word count distribution for a revision of a page.

E.g.

"This is a content.  I have a content." -->
{
  "this": 1,
  "is": 1,
  "a": 2,
  "content": 2
  "i": 1,
  "have" 1
}

Using this, we can get a sense for how unusual a new contribution is.

"This is a content.  I have a content." --> "This is a content.  Content is this."
{
  "content": 0,
  "is": 1,
  "this": 1,
  "a": -1,
  "have": -1,
  "i": -1
}

Using this representation of a word frequency diff, we can detect changes that add new words to the page (probably strongly associated with new meaning) -- e.g. proportional additions and removals. Working with the diff above and comparing to the initial revision, we get the following addition and removal proportions.

additions:
{
  "is": 1,  # +1/1 = increase of 100%
  "this": 1  # +1/1 = increase of 100%
}
removals:
{
  "a": -0.5, # -1/2 = decrease of 50%
  "have": -1, # -1/1 = decrease of 100%
  "i": -1 # -1/1 = decrease of 100%
}

I suspect that this will be particularly valuable for badwords. E.g. if one were to edit the article about a particular curse word (e.g. https://en.wikipedia.org/wiki/Shit), adding a new instance of that curse word to the article would result in a minor proportional change while adding a different curse word would result in a large proportional change.