Part of it is genuine differences between online conversational language versus standard written English, like emoticons, Twitter-specific discourse markers, and hard-to-segment compounds or clitic constructions (see the Gimpel and Owoputi papers (2011, 2013) linked on the page, and/or the annotation guidelines document too). Part of it is just that it's easier for humans to annotate the coarse-grained POS tagset, and we didn't have many resources for annotation when we did it.

These things also intersect ... for example, you'd have to figure out how dialectical English verbal auxiliaries like "finna", or the second or so word in "imma", map to PTB tags. It's possible but just takes more work and thinking through the descriptive linguistics and what you want to use it for. Someday I'd like to update the whole thing for a more PTB-like POS tagset, if it can be done well. I feel like Chris Manning's whitepaper on issues in PTB POS data convinced us (well, it convinced me, at least) that it might be a good idea to focus on making high quality tag annotations. (http://nlp.stanford.edu/pubs/CICLing2011-manning-tagging.pdf )

The default tagset that this employs is much more coarse-grained than the Penn tagset. For example, it has only two verb tags, whereas Penn has separate tags for infinitives, gerunds, past forms, etc. On the other hand, it supports the peculiar kinds of lexemes that abound in tweets, having separate tags for hashtags, @-mentions, URLs, emoticons, etc.

Twitterese English is very different from standard English, and by taking the dedicated-tagset approach that embraces this fact, the authors were able to accurately attribute structure even to text that appears gibberish at first sight. To me, this is very impressive.

People at UWash (Ritter et al.) have Twitter tools with a tagset closer to PTB tagset (they still have special tags for @mentions and #hashtags)