Which (naive) NLP method for correlating human messages in chatrooms?
This is a stylometry task, more exactly some form of author verification/identification task. In case you want to dig deeper, the PAN workshop series is a good source of datasets and methods.
About your method:

Your intuition is correct about selecting the most frequent words, in particular stop words: writing style is better characterized by the patterns in frequent grammatical constructs than by the choice of content words. However depending on the size of the data you might want to be more flexible about the number of words: if you have enough data, you should probably take more than the top 100 frequent words. If you don't, well... it might not work very well. Note also that some successful methods don't use the top frequent words but rather a range of middle frequency words.
Using TFIDF is controversial for style identification. It's often done simply with the term frequency instead.

Other features you could consider:

words n-grams, typically bigrams or even trigrams if there is enough data.
characters n-grams (usually trigrams) have the surprising property to be very robust features for style detection. If you use these do not apply any tokenizer or remove punctuation signs.