Hi, thanks for following the forum. :-) I think you mean "unstructured data". For example, a text document or a tweet do not have a clear structure. In that case, yes, we could do some pattern mining.
For example, you can consider sentences of a text as sequence of symbols (items), and then apply sequential pattern mining to find subsequences of words that appear frequently in tweets or a text document. In my previous work, I for example mined sequential patterns from books to analyze the writing styles of people. In that paper:
Pokou J. M., Fournier-Viger, P., Moghrabi, C. (2016). Authorship Attribution Using Small Sets of Frequent Part-of-Speech Skip-grams
. Proc. 29th Intern. Florida Artificial Intelligence Research Society Conference (FLAIRS 29), AAAI Press, pp. 86-91
In that paper, what we call Skip-grams is basically a sequential pattern.
But for a book it is probably more interesting to find patterns than in short messages like tweets. A tweet is usually very short and people may not write them very well, so it is more challenging to analyze tweets in my opinion than to analyze a book.
Similarly, if we see sequences as bag of words (words without order), than we can apply itemset mining. Each transaction is a sentence and each item is a word. This would find sets of items common to multiple sentences for example.
I think there are various possibilities. I just mention a few that come to my mind.
Perhaps that some other possibilities about social network would be to analyze a matrix of "likes" such as on Facebook, where pages are items, and each user is a transaction. Thus, each transaction would indicates the pages that a user like. Then we could use itemset mining to find some correlation between sets of page that people like together.