Tens of thousands of questions are asked and answered
every day on social question and answer (Q&A) Web sites
such as Yahoo Answers. While these sites generate an
enormous volume of searchable data, the problem of
determining which questions and answers are archival
quality has grown. One major component of this problem is
the prevalence of conversational questions, identified both
by Q&A sites and academic literature as questions that are
intended simply to start discussion. For example, a
conversational question such as “do you believe in
evolution?” might successfully engage users in discussion,
but probably will not yield a useful web page for users
searching for information about evolution. Using data from
three popular Q&A sites, we confirm that humans can
reliably distinguish between these conversational questions
and other  informational questions, and present evidence
that conversational questions typically have much lower
potential archival value than informational questions.
Further, we explore the use of machine learning techniques
to automatically classify questions as conversational or
informational, learning in the process about categorical,
linguistic, and social differences between different question
types. Our algorithms approach human performance,
attaining 89.7% classification accuracy in our experiments.