  In Twitter, and other microblogging services, the generation of new content
by the crowd is often biased towards immediacy: what is happening now. Prompted
by the propagation of commentary and information through multiple mediums,
users on the Web interact with and produce new posts about newsworthy topics
and give rise to trending topics. This paper proposes to leverage on the
behavioral dynamics of users to estimate the most relevant time periods for a
topic. Our hypothesis stems from the fact that when a real-world event occurs
it usually has peak times on the Web: a higher volume of tweets, new visits and
edits to related Wikipedia articles, and news published about the event. In
this paper, we propose a novel time-aware ranking model that leverages on
multiple sources of crowd signals. Our approach builds on two major novelties.
First, a unifying approach that given query q, mines and represents temporal
evidence from multiple sources of crowd signals. This allows us to predict the
temporal relevance of documents for query q. Second, a principled retrieval
model that integrates temporal signals in a learning to rank framework, to rank
results according to the predicted temporal relevance. Evaluation on the TREC
2013 and 2014 Microblog track datasets demonstrates that the proposed model
achieves a relative improvement of 13.2% over lexical retrieval models and 6.2%
over a learning to rank baseline.
