Primary Assumption: Topic-related and person-related metadata is stored in different collections and the collection being discussed here is utilized only to keep track of topic subscribers.
Storing subscribers as a list/array associated with a topic identifier as the document key (meaning an indexed field) makes for an efficient structure. Once you have a topic of interest you can lookup the subscriber list by topic identifier. Here, as @Saleem rightly pointed out, you need to be wary of large subscriber lists causing documents to exceed the 16MB documents size limit. But, instead of complicating the design by making a different collection to handle this (as suggested by @Saleem), you can simply split the subscriber list (into as many parts as required, using a modulo 16MB operation) and create multiple documents for a topic in the same collection. Given that the topic identifier is an indexed field, lookup time will not be hurt, since 16MB can accomodate a significantly huge number of subscriber identifiers and number of splits required should be fairly low, if needed at all.
The other structure you suggested, where a subscriber identifier is the document key with all their subscribed topics in the document is intuitively not so efficient for a large dataset. This structure would involve lookup of all subscribers subscribing to the topic at hand. If subscribed topics are stored as a list/array (seems the likely choice) this query would involve a $in clause which is slower than a indexed field lookup, even for small sized topic lists over a significantly large user base.
