How to create a good list of stopwords I am looking for some hints on how to curate a list of stopwords. Does someone know / can someone recommend a good method to extract stopword lists from the dataset itself for preprocessing and filtering?

The Data:

a huge amount of human text input of variable length (searchterms and whole sentences (up to 200 characters) ) over several years. The text contains a lot of spam (like machine input from bots, single words, stupid searches, product searches ... ) and only a few % of seems to be useful. I realised that sometimes (only very rarely) people search my side by asking really cool questions. These questions are so cool, that i think it is worth to have a deeper look into them to see how people search over time and what topics people have been interested in using my website.

My problem:

is that i am really struggling with the preprocessing (i.e. dropping the spam). I already tried some stopword list from the web (NLTK etc.), but these don't really help my needs regarding this dataset. 

Thanks for your ideas and discussion folks!
One approach would be to use tf-idf score. The words which occur in most of the queries will be of little help in differentiating the good search queries from bad ones. But ones which occur very frequently (high tf or term-frequency) in only few queries (high idf or inverse document frequency) as likely to be more important in distinguishing the good queries from the bad ones.