Twitter serves over 1.6 billion searches each day, ranking tweets for display to the user in reverse-chronological order. However, finding relevant tweets can be a challenging task, since the relevance of a tweet is dependant both on its content and whether it links to a useful document. In this paper, we investigate how the content of documents hyperlinked from a tweet can be used to better estimate that tweet’s relevance. In particular, we propose three approaches for incorporating the content of hyperlinked documents when ranking...
