Text Classification with mixed features in Random Forests
Are you using raw term frequencies, or TF-IDF?

Perhaps you could simply combine the terms in the tweet with the terms in the URL-linked pages (if any) into a single bag of words, calculate TF-IDF, and normalize to avoid bias towards longer documents (i.e., those tweets containing URL links).


  if I decide to use the same term frequency representation also for URLs I will have a huge number of features only from URLs


I don't understand what you mean here. Aren't your features the terms in your bag of words? So the number of features will be the size of your vocabulary, which I imagine won't change much whether you include URLs or not.


  Besides I'll have to fill some impossible values (like -1) for the URL features for tweets that do not have URLs, and I will probably worsen the classification for this tweets, as I will have a huge number of uninformative features.


I don't understand this either. Term-document matrices are virtually always a sparse matrix, since most of the terms in your vocabulary won't appear in most of your documents. So, the vast majority of values in your TDM will be 0. I don't know where you're getting -1 from.