I found the article a bit obtuse, but I take it to mean we are drawing a uniform sample from a very large set. This means we don't need a shuffled sample, we just need one with a uniform chance of each source element being in the sample.
Duplication in the context of the article appears to refer to tweet identity rather than content, so taking a stream of all existing tweets and selecting a uniformly sampled subset of them is certainly possible without storing the list of sampled tweets.
The algorithm referred to has an additional constrain of sequential access only. If you have random access there are simpler approaches to take. If you have an unbounded input stream you just filter using a prng for a uniformly distributed sample.
The article only applies to enormous and sequentially accessed data sources, which is why the algorithm died with tape drives: it's not actually a common problem any more.