Through the Twitter Glass: Detecting Questions in Micro-Text
This work addresses the challenge of understanding Q&A habits on Twitter for researchers or analysts, but it is incremental as it applies existing methods to a new domain without major innovations.
The authors tackled the problem of detecting questions in Twitter micro-text by applying traditional NLP approaches, finding that while Twitter's idiosyncrasies make processing difficult, its length restrictions and simple syntax could aid performance, though results are preliminary.
In a separate study, we were interested in understanding people's Q&A habits on Twitter. Finding questions within Twitter turned out to be a difficult challenge, so we considered applying some traditional NLP approaches to the problem. On the one hand, Twitter is full of idiosyncrasies, which make processing it difficult. On the other, it is very restricted in length and tends to employ simple syntactic constructions, which could help the performance of NLP processing. In order to find out the viability of NLP and Twitter, we built a pipeline of tools to work specifically with Twitter input for the task of finding questions in tweets. This work is still preliminary, but in this paper we discuss the techniques we used and the lessons we learned.