Yahoo Answers (YA) is a large and diverse question-answer
forum, acting not only as a medium for sharing technical
knowledge, but as a place where one can seek advice, gather
opinions, and satisfy one's curiosity about a countless number
of things. In this paper, we seek to understand YA's
knowledge sharing activity. We analyze the forum categories
and cluster them according to content characteristics and
patterns of interaction among the users. While interactions
in some categories resemble expertise sharing forums, others
incorporate discussion, everyday advice, and support. With
such a diversity of categories in which one can participate,
we nd that some users focus narrowly on speci c topics,
while others participate across categories. This not only allows
us to map related categories, but to characterize the
entropy of the users' interests. We nd that lower entropy
correlates with receiving higher answer ratings, but only for
categories where factual expertise is primarily sought after.
We combine both user attributes and answer characteristics
to predict, within a given category, whether a particular answer
will be chosen as the best answer by the asker.ARI
Intel Research
National Science Foundation (0325347)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58015/1/fp840-adamic.pd