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The increasing popularity of Twitter renders improved trustworthiness andrelevance assessment of tweets much more important for search. However, giventhe limitations on the size of tweets, it is hard to extract measures forranking from the tweets' content alone. We present a novel ranking method,called RAProp, which combines two orthogonal measures of relevance andtrustworthiness of a tweet. The first, called Feature Score, measures thetrustworthiness of the source of the tweet. This is done by extracting featuresfrom a 3-layer twitter ecosystem, consisting of users, tweets and the pagesreferred to in the tweets. The second measure, called agreement analysis,estimates the trustworthiness of the content of the tweet, by analyzing how andwhether the content is independently corroborated by other tweets. We view thecandidate result set of tweets as the vertices of a graph, with the edgesmeasuring the estimated agreement between each pair of tweets. The featurescore is propagated over this agreement graph to compute the top-k tweets thathave both trustworthy sources and independent corroboration. The evaluation ofour method on 16 million tweets from the TREC 2011 Microblog Dataset shows thatfor top-30 precision we achieve 53% higher than current best performing methodon the Dataset and over 300% over current Twitter Search. We also present adetailed internal empirical evaluation of RAProp in comparison to severalalternative approaches proposed by us.

RAProp: Ranking Tweets by Exploiting the Tweet/User/Web Ecosystem and Inter-Tweet Agreement