feature importance between two data distributions
My two cents:
I can think of several ways calculating specific terms repressiveness:

1) Calculating rate of documents(tweets/posts) where these terms appear more than 
1(or X > 1) times. You can decide on X by looking at the distribution of frequency per document.
2) Calculating terms TF-IDF score per each document and aggregating across all documents. Aggregating across all document  depends on your need and what you mean by representative.
3) Calculating terms appearance rate - how many times these terms appear divided by total number of words in the corpus(possibly after subtracting stop words).