Suppose an online chatroom is filled with many "alt" accounts - i.e, multiple accounts are being controlled by one user, a troll. This user leverages multiple accounts in order to steer the conversation in particular directions to suit their needs. This user may also be altering their mode of speech per account, to avoid detection.
Suppose I want to detect these alt accounts by using some kind of NLP classifier. What would the best method be? Where "best" means effective, but also relatively simple to set up - as in something you could do in python with relatively basic sklearn modules.
What I've tried so far is to collect the 100 most frequent words used by each user, and just throw that corpus into a sklearn.feature_extraction.text.TfidfVectorizer, and then looking at the pairwise similarity. These are mostly stop words, words which most NLP articles online are telling me to ignore. But I think these basic (almost unconsciously-used) words are less likely to be subject to obfuscation. The user may, for example, be applying varying spellings of less-common words (realize vs realise etc) - but the user could not plausibly have alternative spellings for ["the", "of", "you", "one"] etc.
Like I said, that isn't an "advanced" approach, but it's an example of the general level of complexity that I'm willing to undertake. Is there are any validity to that approach? If not, can you suggest a better one?