  Information retrieval systems are evolving from document retrieval to answer
retrieval. Web search logs provide large amounts of data about how people
interact with ranked lists of documents, but very little is known about
interaction with answer texts. In this paper, we use Amazon Mechanical Turk to
investigate three answer presentation and interaction approaches in a
non-factoid question answering setting. We find that people perceive and react
to good and bad answers very differently, and can identify good answers
relatively quickly. Our results provide the basis for further investigation of
effective answer interaction and feedback methods.
