  The real-time nature of Twitter means that term distributions in tweets and
in search queries change rapidly: the most frequent terms in one hour may look
very different from those in the next. Informally, we call this phenomenon
"churn". Our interest in analyzing churn stems from the perspective of
real-time search. Nearly all ranking functions, machine-learned or otherwise,
depend on term statistics such as term frequency, document frequency, as well
as query frequencies. In the real-time context, how do we compute these
statistics, considering that the underlying distributions change rapidly? In
this paper, we present an analysis of tweet and query churn on Twitter, as a
first step to answering this question. Analyses reveal interesting insights on
the temporal dynamics of term distributions on Twitter and hold implications
for the design of search systems.
