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This research proposes leveraging Twitter features to enhance recency ranking by training a classifier ensemble. It aims to determine if URLs shared by influential Twitter users are more event-relevant than top-ranked search engine URLs. This work delves into utilizing social media data for improved recency ranking algorithms.
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