  The increasing popularity of Twitter renders improved trustworthiness and
relevance assessment of tweets much more important for search. However, given
the limitations on the size of tweets, it is hard to extract measures for
ranking from the tweets' content alone. We present a novel ranking method,
called RAProp, which combines two orthogonal measures of relevance and
trustworthiness of a tweet. The first, called Feature Score, measures the
trustworthiness of the source of the tweet. This is done by extracting features
from a 3-layer twitter ecosystem, consisting of users, tweets and the pages
referred to in the tweets. The second measure, called agreement analysis,
estimates the trustworthiness of the content of the tweet, by analyzing how and
whether the content is independently corroborated by other tweets. We view the
candidate result set of tweets as the vertices of a graph, with the edges
measuring the estimated agreement between each pair of tweets. The feature
score is propagated over this agreement graph to compute the top-k tweets that
have both trustworthy sources and independent corroboration. The evaluation of
our method on 16 million tweets from the TREC 2011 Microblog Dataset shows that
for top-30 precision we achieve 53% higher than current best performing method
on the Dataset and over 300% over current Twitter Search. We also present a
detailed internal empirical evaluation of RAProp in comparison to several
alternative approaches proposed by us.
